Animal Cognition

First published Tue Jan 8, 2008; substantive revision Fri May 6, 2016

The philosophical issues that relate to research on animal cognition
can be categorized into three groups: foundational issues about
whether non-human animals are the proper subject of psychological
investigation; methodological issues about how to study animal minds;
and more specific issues that arise from within the specific research
programs.

While the study of animal cognition is largely an empirical endeavor,
the practice of science in this area relies on theoretical arguments
and assumptions — for example, on the nature of mind,
communication, and rationality. If nonhuman animals don’t have
beliefs, and if all cognitive systems have beliefs, then animals
wouldn’t be the proper subjects of cognitive studies. If animals
aren’t agents because their behavior isn’t caused by propositional
attitudes, and if all cognitive systems are agents, we get the same
conclusion. While there are arguments against animal minds, the
cognitive scientists studying animals largely accept that animals are
minded, cognitive systems. As demonstrated by the 2012
Cambridge Declaration on Consciousness,
many scientists also accept
animal consciousness.

Many of the research programs investigating particular cognitive
capacities in different species raise philosophical questions and have
implications for philosophical theories, insofar as they impose
additional empirical constraints for naturalistically minded
philosophers. Traditional research paradigms in animal cognition are
similar to those in human cognition, and include an examination of
perception, learning, categorization, memory, spatial cognition,
numerosity, communication, language, social cognition, theory of mind
or mindreading, causal reasoning, and metacognition.

Scientists working within any one of these areas might use very
different methods, because there is no one discipline of animal
cognition. For example, social cognition could be studied by a
biologist who documents mutual gaze in mother-infant dyads across
primate species (e.g. Matsuzawa 2006b), an ethologist interested in
free-ranging canid social play behavior (e.g. Bekoff 2001), an
experimental psychologist testing theory of mind in an adult
symbol-trained chimpanzee (e.g. Premack & Woodruff 1978), an
anthropologist observing social games in capuchin monkey communities
(e.g. Perry et al. 2003), or a cognitive neuroscientist
investigating neural basis of gaze-following in primates (e.g. Emery
2000). Finally, findings about the cognitive abilities of animals
often play a role in debates about the
moral status of animals,
as well as in investigations into whether animals may engage in some
sort of moral practice.

1. What is Animal Cognition?

Animal cognition research examines the processes used to generate
adaptive or flexible behavior in animal species. Much of the work on
animal cognition is more appropriately described by the term
comparative cognition, because the processes and capacities underlying
behavior are compared between species (Shettleworth 2010). In the
context of animal cognition research, cognition research usually
focuses on questions about the mechanisms involved in specific
capacities, such as learning, memory, perception, or decision-making.
Researchers also investigate animal concepts, beliefs, and thoughts.
While the
representational theory of mind
is a common assumption among animal cognition researchers, there is
also investigation into the role perception plays in animal learning,
and interest in how much explanatory work can be done by
nonconceptual content,
sometimes inspired by work in
embodied cognition
(e.g. Barrett 2011). And, while cognitive processes are often
contrasted with associative processes, this distinction is often
challenged (e.g. Buckner 2011; Mitchell et al. 2009). As a part of
cognitive science,
research in animal cognition aims to uncover the different cognitive
mechanisms at play across species, with the purpose of understanding
the varieties of cognition, the similarities between humans and other
species, and the evolution and function of cognitive processes.

2. Foundational Issues

The philosophical discussion of animal cognition has been
traditionally focused on the metaphysics and epistemology of mind in
creatures that do not have language. Philosophers have asked whether
animals are minded or rational, and whether they have concepts or
beliefs, but they have also struggled with the issue of how to answer
such questions given the inherent limitations of the
investigation.

The early history of western philosophy reflects a tendency to see
animals as lacking rationality. Aristotle defined “human”
as “the rational animal”, thus rejecting the possibility
that any other species is rational (Aristotle Metaphysics).
Aquinas believed that animals are irrational because they are not free
(Aquinas Summa Theologica). Centuries later, Descartes
defended a distinction between humans and animals based on the belief
that language is a necessary condition for rational mind; on his view
animals are soulless machines (Descartes Discourse on the
Method). Locke agreed that animals cannot think, because words
are necessary for comprehending universals (Locke Essay Concerning
Human Understanding). Following in this tradition, Kant concluded
that since they cannot think about themselves, animals are not
rational agents and hence they only have instrumental value (Kant
Lectures on Ethics).

However, there were also dissenters. Voltaire criticized Descartes’
view, claiming that we can see the animals’ minds, just as we see
human minds, and it is through interacting with animals that we come
to judge that they have emotions, memories, and beliefs (Voltaire
Philosophical Dictionary). Hume was downright dismissive of
the animal mind skeptics when he wrote “Next to the ridicule of
denying an evident truth, is that of taking much pains to defend it;
and no truth appears to me more evident than that beasts are endowed
with thought and reason as well as man. The arguments are in this case
so obvious, that they never escape the most stupid and ignorant”
(Hume Treatise of Human Nature, 176).

2.1 Animal Minds

Despite Hume’s judgment about their worth, much ink has been spilled
developing arguments for the existence of animal minds, which is often
seen as a special version of the
other minds problem.
Three kinds of arguments for other species of mind are: the
argument from analogy, the inference to the best
explanation argument, and the argument from evolutionary
parsimony.

The argument from analogy for animal minds can be formulated as:

All animals I already know to have a mind (i.e., humans) have
property x.

Individuals of species y have property x.

Therefore, individuals of species y probably have
minds.

Versions of the argument differ as to what they choose as the
reference property x, and how they defend the choice of
reference property. The reference property could refer to any number
of things, such as a general capacity (e.g. problem solving), a
specific ability (e.g. language, theory of mind, tool-use), a
behavior, or even brain activity (see Farah 2008 for an example of
this last approach).

The argument from analogy for animal minds is in one sense stronger
than the argument for other minds, insofar as the reference class is
larger. Rather than starting with the introspection of one’s own mind
and then generalizing to all other humans, the argument for animal
minds takes as given that all humans have minds and generalizes from
the human species to other species. Nevertheless, in another sense the
analogical argument for animal minds is weaker, since the strength of
the argument is a function of the degree of similarity between the
reference class and the target class. Humans are probably more similar
to one another than they are to members of another species. While some
researchers working with great apes have expressed concern about the
argument from analogy (Povinelli et al. 2000; Povinelli 2000),
it has been pointed out that those concerns do not accurately target
analogical arguments for the existence of animal minds (Allen 2002).
Furthermore, recent uses of analogical argument to defend animal
consciousness have been based on careful investigation into the causal
powers of the reference property (Varner 2012)

The inference to the best explanation argument for animal
minds rests on the claim that the existence of animal minds is a
better explanation of animal behavior and physiology than those
offered by other hypotheses. A version of this argument can be
formulated as:

Individuals of species x engage in behaviors
y.

The best scientific explanation for an individual engaging in
behaviors y is that they have a mind.

Therefore, it is likely that individuals of species x
have minds.

The inference to the best explanation argument justifies the
attribution of mental states to animals based on the robust predictive
and explanatory power that is gained from such attributions. As the
argument goes, without such attributions we would be unable to make
sense of animal behaviour. This argument relies on ordinary scientific
reasoning; of two hypotheses, the one that better accounts for the
phenomenon is the one to be preferred. Those who offer this sort of
argument for animal minds are claiming that having a mind (whatever
that amounts to) better explains the observed behaviour.

While it is fair to say that most scientists working with animals
think they have minds, they also have a tendency to use the inference
to the best explanation strategy when they disagree about the
cognitive mechanisms at play in a particular species at a particular
time.

The argument from evolutionary parsimony is based on the idea
that closely related species share some physical traits, and this
relationship can offer evidence in favour of a mentalistic causal
explanation in certain cases. This argument can be formulated
differently depending on the notion of parsimony (Sober 2015). In
general, such arguments suggest that the fact that we share some
property with an animal is enough to establish the animal as
(probably) minded if we assume (a) that we share a common ancestor
with the animal and (b) that we should prefer the most parsimonious
explanation of the emergence of that property (de Waal 1999; Sober
2005). However, without knowledge of the mentality of the common
ancestor, such arguments offer little additional evidence (Sober
2014).

Others contemporary philosophers suggest, like Hume and Voltaire, that
we don’t need arguments for other animal minds, because we directly
see minds when we are interacting with them. As we cuddle with our
dogs and wrestle with our cats, we see our animal companions’ minds
just as clearly as we see our infants’ minds when we play with them.
Relying on an argument for other minds always opens the possibility
that the argument turns out to be bad, and the conclusion false. But
no matter what argument we run across, we will not be able to act as
though we deny those minds we see in cats, dogs, and human infants
(Jamieson 1998, 2009; Searle 1994).

2.2 Anthropomorphism and Folk Psychology

Though we might agree that animals have minds, worries arise when we
start to describe what those minds look like. When researchers
attribute mental content to other species, they open themselves to the
charge of anthropomorphism. The term “anthropomorphism”
has a number of different connotations, but most generally refers to
the act of attributing human traits to other animals. Sometimes the
term is used to refer only to psychological traits, and sometimes it
is used to refer to traits that are claimed to be uniquely human (in
which case anthropomorphism is an error by definition). In recent
years, there have been a number of theoretical discussions about the
charge of anthropomorphism itself (including the essays in Daston
& Mitman 2005; Mitchell et al. 1997; and work by Andrews
2009, Andrews & Huss 2014; Asquith 1997; Buckner 2013; Crist 1999;
Fisher 1990, 1991; Keeley 2004; Kennedy 1992; Meketa 2014; Rivas &
Burghardt 2002; Shettleworth 2010b; Sober 2012; Wynne 2004, 2007), and
worries about the use of folk psychology in animal cognition research
more specifically (Penn 2011; Andrews forthcoming).

One way of seeing an anthropomorphic error is as a category mistake,
rather than as a false attribution. An anthropomorphic error must be
logically false, because members of the target species are not the
sorts of things to which the term can apply (Keeley 2004; Fisher
1991). However, if the charge of anthropomorphism is the charge that
the attributer is making a category mistake, then the charge is being
made on conceptual, rather than empirical grounds. Thus, one response
to the charge of anthropomorphism is continued research, for one
wouldn’t know whether a property is anthropomorphic until after the
relevant research has been completed. As Sober puts it, “The
only prophylactic we need is empiricism” (Sober 2005, 97).

However, Sober also argues that the empirical methodology of
psychology places a greater burden of proof on animal cognition
research than it does on human cognition research. He suggests that
comparative psychologists accept as the null hypothesis that different
cognitive mechanisms are at work in humans and animals. Given that
type 1 errors (reporting a false positive and rejecting a (possibly
true) null hypothesis) are taken to be more serious errors than are
type 2 errors (reporting a false negative and not rejecting a null
hypothesis when it is false), the practice of science results in a
bias against attributing psychological traits to animals (Sober
2005).

The debate about how to interpret the results of animal studies in
comparison to human studies can be viewed as a debate about an
inconsistent application of what the psychologist C. Lloyd Morgan
advanced as his Cannon. Morgan’s Canon states: “In no case may
we interpret an action as the outcome of the exercise of a higher
psychical faculty, if it can be interpreted as the outcome of the
exercise of one which stands lower in the psychological scale”
(Morgan 1894, 53). Though this is a longstanding rule of thumb in
animal cognition research, sometimes referred to as the
“principle of conservatism,” it is not a principle
commonly used in human cognition research. To complicate matters,
attempts to determine which psychical faculties are higher or lower, a
task that Morgan’s Canon instructs a researcher to perform, have
raised worries about its meaningfulness and usefulness
(Allen-Hermanson 2005; Fitzpatrick 2008; Sober 2005).

Some argue that anthropomorphism is a human tendency that must be
overcome in order to do good science, because it relies an unjustified
generalization from linguistic humans to nonlinguistic animals. These
critics suggest that animals who lack language may not even have
concepts, and without language scientists are not in a position to
attribute content. Since we are barred from making attributions,
scientific psychology ought not engage with questions about animal
mentality (e.g. Keeton 1967; Kennedy 1992; Blumberg & Wasserman
1995; Wynne 2004, 2007).

It has been noted that such arguments concern the proper methods of
science, the scope of science, and how to interpret data (Keeley 2004;
Bekoff & Allen 1997). As such, they are not empirical, but
theoretical or methodological arguments. This can be seen in the way
in which the debates sometimes result in an impasse. Those opposed to
attributing mental properties to animals are accused of begging the
question (Griffin 1992), by committing “reverse
anthropocentrism” (Sheets-Johnstone 1992),
“anthropodenial” (de Waal 1999), or
“anthropectomy“ (Andrews & Huss 2014).

Such worries arise in the formulation of the null hypothesis in
experimental research. Due to the standard statistical methods used in
animal cognition experiments, choice of the null hypothesis in terms
of an animal lacking a cognitive capacity biases the research against
finding evidence in favour of that capacity (Andrews & Huss 2014;
Mikhalevich 2014; Sober 2005). One concern is that researchers may
have a failure of imagination when it comes to hypothesis generation;
they may make an inference to the best explanation argument without
considering enough plausible explanations. This reflects Kennedy’s
worry when he claims that the following argument for attributing
mental properties to animals rests on a false dichotomy: either
animals are stimulus-response machines, or they are agents with
beliefs and desires; since animals are not stimulus-response machines,
they must be psychological agents (Kennedy 1992). According to
Kennedy, the problem with this argument is that not all machines
implement stimulus-response functions; some machines are complex and
indeterministic, and if animals were machines, they would be machines
of that sort (Barlow 1990; Kennedy 1992). Similar concerns are put
forward by those who stress, contra Darwin, the discontinuity between
humans and other animals (Penn et al. 2008).

Finally, some worry that the science of comparative cognition has been
damaged by the idea that explanation of behavior in terms of
species-typical predispositions rather than in terms of human-like
reasoning or insight amounts to a “killjoy” explanation
(Shettleworth 2010b). When one considers differences between humans
and animals to be less joyful than similarities, it should not be
surprising if more similarities are found. Even worse is the worry
that some “human-like reasoning” postulated to explain
human behavior is false, and that extraordinary behaviors performed by
humans and other animals might arise out of simple processes (Andrews
2005, 2012; Barrett 2011; Buckner 2013; Penn et al. 2008;
Shettleworth 2010b; de Waal & Ferrari 2010). The idea that human
cognitive capacities are often exaggerated and over-intellectualized
is inspired by work in various fields, including approaches in
cognitive science that focus on the power of simple rules and the
emergence of complex behavior in self-organizing systems,
emergent properties,
mental representation,
artificial life,
robotics, as well as situated, distributed and dynamical approaches
to cognition, the heuristics and biases literature in social
cognition, as well as dual-process accounts of cognitive architecture.
Buckner calls the error of comparing animal cognitive capacities
against an exaggerated picture of human cognitive capacities
“anthropofabulation“, since it involves two errors:
confabulating human cognitive mechanisms plus anthropocentrism
(Buckner 2013).

The concerns about anthropomorphism appear to be largely limited to
western scientists. It has been argued that researchers from countries
with a Buddhist rather than Christian orientation are not culturally
encouraged to see a categorical distinction between humans and
nonhuman animals (Asquith 1991; Sakura 1998; Matsuzawa 2003; de Waal
2003). Unlike Christianity, the Buddhist doctrine does not claim that
humans, but not animals, have immortal souls, and thus it does not
allow humans to use animals for their own purposes in the ways
Christianity does. The Buddhist tradition sees a connection between
humans and other animals, and even states that humans can be reborn as
animals. De Waal argues that the difference in cultural attitudes
toward animals led Western primatologists to first reject Japanese
methods, findings, and ideas; it is only recently that some of those
ideas, such as Kinji Imanishi’s claim that primates display cultural
differences within species, have made their way into Western
scientific discourse (de Waal 2003).

2.3 Rationality

Rational actions are actions that fits together in some way. While often
thought of as the result of logical reasoning, rational action might also be
understood as teleological, causal, or probabilistic. For a collection
of essays on rationality across species see Hurley & Nudds
(2006).

The Stoic philosopher Chrysippus suggested that we can see logical
reasoning in animal behavior in his story about the dog who, running
nose to the ground, tracked a rabbit down a path. When the dog arrives
at a three-way crossroads, he quickly sniffs the first two paths, and
not finding the scent in either of the first two options, immediately
runs down the third path, without sniffing first. This story suggests
that Chrysippus’ dog was able to make a rational inference of the
form:

A or B or C.

Not A.

Not B.

Therefore, C.

However, the dog may have solved the problem without full-blown
logical reasoning. Minimal theories of rationality offer alternatives.

Some minimal theories of rationality stem from evolutionary thought.
For example, on Fred Dretske’s view, even some simple learned
behaviors, such as a bird’s avoiding eating a monarch butterfly, can
be construed as minimally rational. Because monarchs who eat toxic
milkweed become toxic to birds and other predators, when a bird learns
not to eat monarch butterflies after having formed an association
between eating monarchs and vomiting, it has a reason for its
avoidance behavior. The birds also have a reason to avoid eating a
viceroy, given that it is visually almost indistinguishable from a
monarch, though not poisonous. The behavior in both cases is explained
by the content of the bird’s thought (or “thought”), and
for Dretske this is sufficient for the bird to count as a minimally
rational agent (1988, 2006). Other theories of rationality that take
evolutionary considerations into account include those of Daniel
Dennett (1995, 1987), Ruth Millikan (2004, 2006), and Joelle Proust
(1999, 2006).

Causal accounts of animal rational action portray the animal as
engaging in causal, rather than propositional reasoning. José
Bermúdez argues that not all animal action can be explained
merely in terms of minimal rationality or teleology (2003, 2006).
Rather, there are some behaviors that can only be explained in terms
of propositional attitudes, informational states, or generalizations
that go beyond the here and now. However, since animals cannot engage
in metacognition by thinking thoughts about thoughts, they cannot have
the concepts of inference needed for logical reasoning. Rather, we can
describe their reasoning process in causal terms. Consider a gazelle
who see a lion and then runs away. Gazelles can understand that lions
cause them to run, and that since there is a lion here I run. This
causal understanding is developed through experience with regularities
in the environment, and while they are able to generalize to a certain
extent, this ability is limited.

Probabilistic accounts of animal rationality also are used to explain
complex animal behavior. For example, as an explanation of the
Chrysippus’ dog case, Michael Rescorla suggests that the dog could
have employed Bayesian reasoning, and can form and update
probabilities given changes in perceptual information (Rescorla 2009).
Predictive coding
models of cognition that do not rely on the linguistic processing
model associated with the
computational theory of mind
may be used to explain animal behavior as rational on par with human
rationality.

Rationality in other species has been explored in experimental and
naturalistic studies. Psychologists have formally tested animals in
Chrysippus dog type situations. For example, great apes appear to
engage in exclusion reasoning, when they know that one of two opaque
containers are baited with food, and are shown that the empty one is
empty, they will immediately reach for the second container, without
looking inside (Call 2004, 2006; Marsh and Macdonald 2011; Erdőhegyi
et al. 2007). There is evidence that monkeys, corvids, and dogs also
can, in some cases, choose by exclusion.

Certain naturalistic behaviors also suggest rational thought, given
that they appear to be cases of problem solving that rely on cognitive
flexibility and learning. Tool use, for example, is a behavior that
suggests rational thinking. Because tool use involves finding or
constructing an object that is utilized as an extension of the body to
achieve a specific goal, tool use involves identifying a problem,
considering ways of solving the problem, and realizing that other
objects can be used in the manipulation of the situation. Early
experimental research on chimpanzee problem solving by the German
psychologist Wolfgang Köhler had chimps constructing tools to
acquire out-of-reach objects; it was reported that chimpanzees would
stack boxes or put together tubes to form a long rod in order to reach
bananas hung overhead (Köhler 1925). Given this behavior,
Köhler suggested that chimpanzees solve some problems not by
trial and error or stimulus-response association, but through a flash
of insight. (But see Povinelli (2000) for a critique of the
contemporary interpretation of Köhler’s research).

Naturalistic studies of tool use in animals took off in the 1960s,
when two independent research teams in Tanzania observed chimpanzees
making and using tools. Goodall found chimpanzees in Gombe using
grasses and twigs to fish for termites, and she observed chimpanzees
modifying twigs by stripping off their leaves so they could be used
for this purpose (Goodall 1986). Around the same time, Kinji
Imanishi’s team found chimpanzees using rocks to crack nuts in the
Mahale forest, about 200 miles away from Goodall’s research site
(Nishida 1990).

We now know that chimpanzees make and use tools for a number of
different purposes. Chimpanzees at Fongoli, Senegal manufacture spears
in four or more steps in order to hunt bush babies (Preutz &
Bertolani 2007). Chimpanzees at Bossou, Guinea, use large branches
from palm-oil trees to crack open the tree from its crown in order to
gain access to a rich food source (Yamakoshi & Sugiyama 1995).
Chimpanzees also construct and use sets of tools that they
subsequently utilize in a determinate order; Goualougo chimpanzees
will manufacture a perforating tool to enlarge holes in a termite nest
after an unsuccessful fishing attempt; as soon as the exit hole is
enlarged, the chimpanzee then inserts a fishing probe (Sanz and Morgan
2007). Chimpanzees also use tool composites, such as a hammer and
anvil, to crack nuts (Nishida 1990; Sakura & Matsuzawa 1991) and
they manufacture stone tools (Carvalho et al. 2008).

Tool use in the wild has been discovered across taxa, including
invertebrates such as the octopus, birds, fish, amphibians, reptiles,
non-primate mammals, monkeys, and great apes (Shumaker et al. 2011).
Reports of animal tool use offer evidence in favor of the claim that
some animal behavior is functionally rational, in the sense that its
behavior allows the animal to achieve a goal. Furthermore, it is
perhaps the result of an evolutionary adaptation. However, the extent
to which such evidence addresses the question of whether the behavior
is rational is going to depend on one’s view about the nature of
rationality.

2.4 Belief

The question of whether animals have
beliefs
is also difficult to answer given the lack of theoretical agreement
about the nature of belief. The most common view is that belief is a
representational state,
and that the mental representation, which fixes content, expresses
propositional content.
For some, this view is consistent with animal belief, since they
believe that, like humans, animals can operate in a
Language of Thought
(Fodor 1975; Cheney & Seyfarth 2007; Tetzlaff & Ray 2009).
Some representationalists have suggested that animals might have
representational belief that cannot be expressed propositionally
(Bermúdez 2003; Camp 2009; Rescorla 2009). For example, Camp
suggests that some beliefs are based on imagistic representational
systems, such as diagrams or maps, and that such systems can account
for baboon social knowledge, so there is no need to posit a baboon
language of thought (Camp 2009; in response to Cheney & Seyfarth
2007). While Cheney and Seyfarth claim that the baboon cognitive
system is vehicled by a language of thought given its nature as
representational, discrete-valued, hierarchically structured,
rule-governed, open-ended, and independent of sensory modality, Camp
argues that the inference is not warranted, since non-sentential
representational systems can also have such features. Baboons can have
beliefs without having beliefs with propositional content.
Bermúdez also argues that animals have non-propositional
beliefs, because some animal behavior is only explicable in terms of
beliefs and desires. However, Bermúdez denies that animal
belief has any structure; rather, on his account animal beliefs are
simple imagistic representations of things in the world that permit
some thoughts about others’ goals and perceptions (Bermúdez
2003). The view that animals can have perceptual beliefs that ought
not be understood in terms of propositional attitudes has also been
defended by Glock (2000, 2010).

Representationalists of all sorts point out the limitations in the
different kinds of representational systems. Camp, who defends the
claim that maps have a rich syntactic structure, admits some
limitations: mental maps do not seem to be able to accommodate some
first order thoughts such as non-specific existentially quantified
propositions or universally quantified propositions, or any
second-order thoughts (Camp 2007). Like Camp, Bermúdez defends
the position that, while animals can think, they cannot hold beliefs
about beliefs, but he denies that imagistic thought has syntactic
structure, and hence he concludes that animals are not logical
thinkers (Bermúdez 2003, 2009). Carruthers also argues that,
while even insects have beliefs (Carruthers 2004), there is no
evidence that any nonhuman species has metacognitive abilities
(Carruthers 2008). Carruthers suggests that the evidence from animal
behavior supports the position that belief, like desire, comes in
degrees (Carruthers 2008).

For others, the lack of an external language (and the lack of an error
theory to account for why animals would have a language of thought
while lacking external language) suggests that animals do not have
beliefs at all (Dennett 1996; Davidson 1982, Stich 1978). Donald
Davidson has offered an argument against animal rationality based on
an association between concepts, beliefs, and language. On Davidson’s
view, believers must have the concept of belief, because in order to
have a belief, one must recognize that beliefs can be true or false,
and one cannot understand objective truth without understanding the
nature of beliefs. In order to develop an understanding of objective
truth, one must be able to triangulate with others, to talk to others
about the world, and hence all believers must be language users. Since
other species lack language, they do not have beliefs (Davidson 1982).
Davidson also argues against animal beliefs based on the claim that
having a notion of error is necessary for counting as a believer
(Davidson 1975). These arguments have been widely discussed in the
literature.

A different argument against animal belief has been presented by
Stephen Stich, who argues that we cannot attribute propositional
attitudes to animals in any metaphysically robust sense, given our
inability to attribute content to an animal’s purported belief (Stich
1978). On Stich’s view, if attribution of belief to animals is
understood purely instrumentally, then animals have beliefs. However,
if attribution of beliefs to animals requires that we can accurately
describe the content of those beliefs, then animals don’t have
beliefs. In response to Armstrong’s suggestion that we can fix the
content of animal belief de re (Armstrong 1973), Stich argues that we
cannot make de re attributions because this would violate the
truth-preserving role of attribution. In addition, because
“nothing we could discover would enable us to attribute content
to an animal’s belief” (Stich 1978, 23), we are unable to make
de dicto attributions to other species. Hence, we can make no
attribution, and if we can’t say what an animal’s belief is about, it
makes no sense to say that an animal has a belief. The worry here is
similar to the worry about anthropomorphism; when we use our language
to ascribe content to other species, we may be attributing to them
more than is appropriate. Stich is concerned that when we say
“Fido believes there is a meaty bone buried in the
backyard” we are attributing to Fido concepts he cannot
possibility have, concepts like “backyard” which are only
comprehensible if one has corresponding concepts such as
“property line”, “house”, “fence”,
and so on. Stich’s argument can be formulated as:

In order for something to have a belief, it must have a
concept.

In order to have a concept, one must have particular kinds of
knowledge, including knowledge of how the concept relates to other
concepts.

Non-human animals don’t have such knowledge.

Therefore, non-human animals don’t have beliefs.

There are at least three ways to respond the arguments that beliefs
require concepts which require language. One can deny the necessary
connection between concepts and language, as we saw above; also, as we
will see in the next section some argue that concepts can be had by
individuals who lack language (Allen & Hauser 1996; Allen 1999;
Glock 2010). A second is to deny the necessary connection between
beliefs and concepts; given
nonconceptual content,
belief may be possible without having concepts (Bermúdez 2003;
Glock 2010). A third path is to be an instrumentalist about belief,
such that being able to attribute content that allows for successful
predictions of behavior is sufficient for having belief, even in the
absence of language (Dennett 1995), or by attributing propositional
content that tracks the content of animal minds (Rowlands 2012). One
may go so far as to argue that beliefs need not be understood in terms
of
mental representations.
In addition, given that some theories of content determination do not
rely on linguistic abilities (e.g.
teleological,
causal), one may attempt to sidestep the kinds of concerns
raised by Davidson and Stich by adopting such accounts.

2.5 Concepts

While some philosophers defend the metaphysical claim that natural
language is necessary for having concepts (Brandom 1994, Davidson
1975, Dummett 1993), and while others defend the epistemic claim that
that language possession is a necessary condition for identifying
concepts (Stich 1978), empirical research in animal cognition suggests
that both views may be mistaken. Researchers seek to uncover the
nature of animal concepts both de re and de dicto
using a variety of methods.

One of the earliest methods for examining animal concepts came out of
a series of experiments with pigeons. The subjects were shown
photographs, and were trained to peck at the pictures that contained a
target object, such as a tree, and not respond to the pictures that
didn’t contain the target object. After the training period, the
pigeons were able to generalize to new photographs, pecking only at
those that contained trees just as in the training set. It was
suggested that this sorting ability demonstrates that the pigeon has a
concept of the target object (Herrnstein 1979; Herrnstein et
al. 1976).

Many reject the idea that being able to sort objects is sufficient for
having a concept corresponding to the sortal, since humans can sort
objects while lacking the corresponding concept. As Colin Allen and
Marc Hauser put it, “It is possible to teach a human being to
sort distributors from other parts of car engines based on a family
resemblance between shapes of distributors. But this ability would not
be enough for us to want to say that the person has the concept of a
distributor” (Allen & Hauser 1996, 51).

Rather than identifying concept acquisition with sorting behavior,
Allen and Hauser suggest alternative methodologies for identifying
concepts in other species. For example, they offer a possible (though,
they admit, ethically untenable) test for a death concept among vervet
monkeys (Allen & Hauser 1996). Vervet mothers are capable of
recognizing the alarm cries of their infants, and when they hear such
a cry the mother will look towards her infant, and the other females
will look towards the mother. Allen and Hauser suggest that playing a
recording of a recently deceased infant’s alarm cry would help to
determine whether vervets have a concept of death. If the mother
responds to these recordings in an atypical fashion, unlike the usual
response made to a living infant, that response provides evidence that
vervet monkeys have the concept of death. According to Allen and
Hauser, having a concept permits different responses to identical
stimuli. The actual sound of the infant’s alarm cry during life is
identical to the sound played back after death. If the response to
this stimulus is different, this is evidence that there has been a
conceptual change associated with the stimulus. Allen presents the
general strategy for attributing concepts to animals as follows:
“An organism O may reasonably be attributed a concept
of X (e.g. TREE) whenever:

O systematically discriminates some Xs from some
non-Xs; and

O is capable of detecting some of its own discrimination
errors between Xs and non-Xs; and

O is capable of learning to better discriminate
Xs from non-Xs as a consequence of its
capacity”(Allen 1999, 37).

Another method for studying animal concepts comes from research on
human infants (Hauser et al. 1996; Hauser & Carey 1998;
Bermúdez 2003; Gómez 2005, Uller 2004). The preferential
looking time paradigm, also known as the dishabituation paradigm, is
used to study human infants’ understanding of the physical and social
world (Baillargeon & DeVos 1991; Spelke 1991). Dishabituation
experiments are thought to help us understand what kinds of
predictions infants make about their world, and this information can
help us determine how they see the world. The methodology is simple;
an infant is repeatedly shown a stimulus, and as soon as he becomes
habituated to the stimulus, the infant becomes disinterested. At this
point, a new stimulus is shown. If the infant sees the new stimulus as
different from the target stimulus, or impossible given the target
stimulus, the infant will look longer at the new stimulus. If the
infant takes the new stimulus to be similar to the target stimulus,
then she will not show any additional interest. The idea is that by
comparing responses among groups of individuals, a researcher can
learn something about how that group conceptualizes the world.
Bermúdez argues that such methods can be used to make de dicto
ascriptions to animals (Bermúdez 2003 b).

In one study using this method, Marc Hauser and colleagues
investigated numerical concepts in different primate species,
including rhesus monkeys (Hauser et al. 1996) and cotton-top
tamarins (Uller 1997). The researchers tested the monkeys’ ability to
keep track of individual objects placed behind a barrier. They found
that, like human infants, the monkeys look longer at impossible
outcomes. For example, in one test condition the rhesus monkeys were
shown two eggplants serially placed behind a screen, and then the
screen was lifted showing only one eggplant. The monkeys looked longer
at the one eggplant than they did when shown the two eggplants,
suggesting that they represent the eggplants as distinct sortals.

Another way we might learn how different species organize the world is
to teach individuals a symbolic communication system. For example, the
biologist Irene Pepperberg trained an African Grey parrot named Alex
to sort objects using meta-level concepts that categorize other
concepts. Alex was able to sort objects according to color, shape, and
matter, and he was able to sort sets of objects according to number.
In addition to sorting, Alex could report which feature makes two
objects similar or different. For example, when presented with a red
block and a red key, Alex responded to the question “What’s
same?” by uttering “color.” He could also report
similarities and differences in shape and matter. Pepperberg claims
that her studies demonstrate Alex’s understanding of categorical
concepts, and reveal the classifications that Alex devised (Pepperberg
1999). However, one might be worried that the concepts exhibited by
symbol-trained animals are an artifact of the communication system,
and not typical of the species.

Finally, animal concepts are also being studied in the field, where
the concepts’ usefulness to animals may be more apparent. Through
careful observation and field experiments of wild individuals,
researchers are examining the natural concepts different species may
use in order to categorize events in their social and physical
environment. For example, primatologists observed that Diana monkeys
have acoustically distinct alarm calls for different predators, and
that the female alarm calls differ from the male (Zuberbühler
et al. 1997; Zuberbühler et al. 1999). This natural
difference allowed Zuberbühler and colleagues to set up a field
experiment with the purpose of determining whether the male and female
alarm calls mean the same thing to the monkeys. He found that they
treated the acoustically different calls as synonyms. Such evidence
suggests that researchers can identify at least some animals’ concepts
“de re”.

Social knowledge offers a window for field researchers who investigate
animal concepts. Cheney and Seyfarth argue that primate behavior
relies on a rich body of social knowledge, and that this knowledge
suggests that primates have conceptual understanding (Cheney &
Seyfarth 2009, 2015). Taking the case of baboons, we know that they
recognize individuals, classify them into groups according to
properties including close social bonds, kinship, dominance ranks, and
transient sexual relations. For example, knowledge of kinship is
demonstrated in instances of kin-mediated reconciliation, when an
antagonistic encounter is resolved by a kin of the aggressor giving a
reconciliation grunt. This categorical information informs baboon
behavior. In addition, some of these relationships change over time,
and can have widespread repercussions over the group dominance
organization. Baboons are able to quickly make adjustments about
linear dominance ranks after a rank reversal, even when the reversal
affects different matrilines and causes changes in the rank
relationship of several individuals. Cheney and Seyfarth argue that
memory and classical conditioning alone cannot account for the
richness of primate social knowledge, given the amount information
primates would have to represent — they claim that a baboon
would have to learn thousands of dyadic relations, and tens of
thousands of triadic relations in order to anticipate other animals’
behavior. In addition to worries about the space needed to represent
all those relations, they point out that the speed of baboon behavior
in response to a complex problem is not consistent with the hypothesis
that baboons solve social problems by searching through a humongous
and unstructured database of relations. Rather, Cheney and Seyfarth
suggest that baboons and other primates with complex social societies
organize individuals into rule-governed classes, or concepts. This,
they argue, is an adaptive strategy (Cheney & Seyfarth 2009).

While much of the discussion has focused on whether animals have
concepts and how concepts can be identified, it has also been argued
that the animal evidence suggests that we may better represent
cognitive diversity and individual complexity by considering that
there are different kinds of representations, and not all of them are
conceptual. Some animal behaviors may rely on
nonconceptual content
which permits flexible behavior but does not require the ability to
identify objects, while other behaviors rely on perception-based
concepts and even propositional content (e.g. in the case of
symbol-trained apes) (Newen & Bartels 2007).

The idea that if we can’t say what animals think, then animals
don’t have beliefs has been challenged by appealing to nonconceptual
content. Jacob Beck (2013) suggests that we cannot say what an animal
thinks because animals think in a nonlinguistic format. Just as a
painting can have content without having content that is expressible
in language, animals may have content that is not expressible in
language. Furthermore, humans may share some of this content with
animals, and so we could share beliefs with an animal if we think in
the same nonlinguistic format about the same thing. The nonlinguistic
format may be an analog one, rather than in a digital format such as
language. Analogue formats do not permit analysis in terms of how
meaningful parts fit together to make a meaningful whole, the way
language does. Instead, analogue formats have their meaning
holistically, but like a photograph analog representations come in
degrees of focus. Examples of analog content include pictures, images,
and maps, and since we cannot translate from such representations to a
sentence (Beck asks us to consider how to translate the Mona Lisa into
English!), we won’t be able to translate animal representations into
language. But animals can still have representations, just as the Mona
Lisa still exists despite the fact that it is untranslatable. Beck
provides an example of nonconceptual content shared by humans and
animals in the case of analogue magnitude states, which are
representations of approximate number (Beck 2012). Humans and other
species can make judgments about greater or lesser arrays of objects
so long as the ratio is large enough. Beck argues that these
approximate number representations cannot be accurately expressed in
sentences, and concludes that this offers evidence in favor of
non-conceptual content for animals and humans alike. Analogue
magnitudes are examples of perceptual representational states, which
do not have the same logical properties as sentential representational
states.

3. The Science of Animal Cognition

Today, no one discipline has a monopoly on the study of animal
cognition. Psychology, biology, anthropology, animal welfare,
philosophy, animal studies, and other programs all include researchers
working on animal cognition. Just as no one discipline has an
exclusive focus on animal cognition, there is no one method that
characterizes research in animal cognition. While early scientific
interest in animal behavior was approached differently by the
psychologists, who focused on using laboratory experiments to uncover
mechanisms, and ethologists, who used observational methods to uncover
the evolution of behavior, today many scientists approach the study of
animal cognition by using a combination of methods.

3.1 Early Anecdotal Method

Contemporary scientific interest in animal minds and cognitive
capacities is often traced back to Charles Darwin’s theory of
evolution by natural selection. In The Descent of Man, Darwin
introduced many of the issues that motivate the research programs in
animal cognition today, including tool use, reasoning, learning,
concepts, consciousness, social cognition, artistic abilities, and
moral cognition. In addition, Darwin anticipated current interest in
implicit reasoning with his comment “The savage would certainly
neither know nor care by what law the desired movements were effected;
yet his act would be guided by a rude process of reasoning, as surely
as would a philosopher in his longest chain of deductions”
(Darwin 1974, 75).

Like Aristotle centuries before him, Darwin advocated the continuity
of the mental across species; just as some morphological
characteristics are homologous across species living in similar
environments, we should expect psychological and behavioral
similarities as well: “the difference in mind between man and
the higher animals, great as it is, certainly is one of degree and not
of kind” (Darwin 1974, 122). This view was also advocated by
Darwin’s contemporary, the naturalist George Romanes, who in his book
Animal Intelligence writes “there must be a
psychological, no less than a physiological, continuity extending
throughout the length and breadth of the animal kingdom”
(Romanes 1970, 10). Darwin’s view about continuity across species has
been challenged on several dimensions, from the notion that language
or culture plays a fundamental role in the evolution of cognition to
worries about anthropomorphism (for discussions see Andrews &
Radenovic 2012; Penn et al. 2008; Shettleworth 2010b).

The method that Darwin, Romanes and their contemporaries first used to
investigate these questions could be described as the anecdotal
method. Stories about animal behavior were collected from a variety of
people, including military officers, amateur naturalists, and layfolk,
and were compiled and used as evidence for a particular cognitive
capacity in that species. This approach was widely criticized. The
“evidence” gathered was often a story told about an event
witnessed by a single person, usually not a trained scientific
observer. In addition, these stories were often acquired second- or
third- hand, so there were worries that the reports had been
embellished or otherwise altered along the way. These problems were
recognized early on, and in response Romanes developed three
principles for accepting anecdotes in order to avoid some of these
problems:

Never accept an incident report as fact without considering the
authority or respectability of the observer.

If the observer isn’t known, and the incident report is
sufficiently important, consider whether the observer may have reason
or cause to make an inaccurate report.

Look for corroborations of the observation by examining similar or
analogous observations made by other independent observers (Romanes
1970).

The third principle was the one he most relied on, writing “This
principle I have found to be a great use in guiding my selection of
instances, for where statements of fact which present nothing
intrinsically improbable are found to be unconsciously confirmed by
different observers, they have as good a right to be deemed
trustworthy as statements which stand on the single authority of a
known observer, and I have found the former to be at least as abundant
as the latter” (Romanes 1970, ix).

Despite Romanes’ attempts, the method remained problematic insofar as
it didn’t provide any statistical information about the frequency of
such behaviors; selection bias would lead people to report only the
interesting intelligent behaviors and ignore the frequency of
behaviors that might serve as counterevidence. Thus, the anecdotal
method as practiced by Darwin and Romanes lacks many of the virtues
associated with good scientific methods.

Though the early anecdotal method has been rejected, some researchers
argue that anecdotes play an important role in animal cognition
research (Bates & Byrne 2007), and observation of behavior is
still considered a valuable research tool, as we will see below.

3.2 Experimental Methods

The British biologist and psychologist C. Lloyd Morgan, who is often
credited with the rise of contemporary animal cognition methods, was
critical of Romanes’ methods. He noted that animal behaviors that are
interesting to us could be caused in various ways, which makes the
search for mechanisms causing behavior the important task for an
animal psychologist. Morgan asked us to consider the example of Tony,
a fox-terrier pup who was able to open the gate from his garden and
escape into the road. Tony succeeded at this task by putting his head
under the latch of the gate, then lifting the latch and waiting for
the gate to swing open. Morgan suggests one might interpret Tony’s
behavior in terms of him having a goal and knowing how to achieve that
goal, but because there are other interpretations, it would be too
hasty to draw that conclusion. Perhaps instead Tony was responding to
the visual affordances of the gate, seeing the latch as liftable
without having the goal to get out of the garden. A third possibility is that was using general reasoning
principles to open the gate, and Tony’s general knowledge was
applied to this specific situation. It is only this third
interpretation that Morgan categorizes as rational, given his view
that rational thought is conceptual thought that permits analysis via
general principles.

Morgan concludes that Tony’s behavior ought not be interpreted as
rational because Tony’s behavior is fairly interpreted as not requiring that he forms some mental concept of how to solve the task. This case serves as an example of Morgan’s canon: “in no case is an animal activity to be
interpreted in terms of higher psychological processes, if it can be
fairly interpreted in terms of processes which stand lower in the
scale of psychological evolution and development” (Morgan 1903,
292). Morgan’s Canon is an epistemic principle that advises to
explain a behavior in terms of the lowest cognitive capacity possible,
and Morgan thinks that reasoning in terms of sense experience is
lower, and that reasoning conceptually in terms of general principles
is a higher psychological process.

Despite a common interpretation of Morgan’s Canon as a
behavioristic rule, Morgan was committed to the existence of animal
minds. In his autobiography, he wrote, “…throughout the whole
investigation, from first to last, my central interest has been
psychological as I understand the meaning of this word. My aim has
been to get at the mind of the chick or the dog or another, and to
frame generalizations with regard to mental evolution” (Morgan 1930,
249). Furthermore, Morgan, like Romanes, advocated for the attribution
of human mental activities to animals using the method of
interpretation via introspection, and he noted the necessity of
interpreting animal behavior. However, at the same time he cautioned
us against thinking that behaviors that appear to be clever, whether
human or animal behaviors, are actually clever. Morgan wrote, “To
interpret animal behavior one must learn also to see one’s own
mentality at levels of development much lower than one’s top-level
of reflective self-consciousness. It is not easy, and savors somewhat
of paradox” (Morgan 1930, 250). This commitment, which has been
dubbed Morgan’s Challenge (Andrews 2015), arises from Morgan’s
recognition that it is difficult for humans to follow his advice not
to over-intellectualize human cognition. The error we risk by not
meeting Morgan’s Challenge has been recently dubbed
“anthropofabulation” by Cameron Buckner, given that the error
involves looking at animals from an anthropocentric perspective and
confabulating the mechanisms humans use (Buckner 2013).

The Clever Hans scandal of 1904 demonstrated Morgan’s Canon in use;
Hans was a famous Russian trotting horse who charmed crowds by showing
an ability to calculate mathematical problems, as well as to read
German and musical notation, simply by tapping his hoof (Candland
1993; Pfungst 1965). After much investigation, the experimental
psychologist Otto Pfungst found that Hans wasn’t counting or reading
language, but rather he was reacting to his owner von Osten’s bodily
motions. Von Osten was unconsciously cuing Hans to start and stop
tapping his foot at the correct time, and Hans had merely leaned to
associate von Osten’s movements with the correct behavior. Today, the
legacy of Clever Hans can be seen in the control methods used during
the experimental testing of an animal’s ability. For example,
researchers who know the correct response will wear a welder’s mask,
blackened goggles, or some other device to keep the subject from being
cued by eye gaze or facial expressions. Another method is to use naive
trainers during testing.

Around this time, other experimental psychologists in the United
States and Russia were interested in uncovering principles of
learning. In the US, Edward Thorndike (1874–1949) adopted Morgan’s experimental method but
rejected his appeal to introspection. Thorndike worried that
introspection is unscientific, because it is unobservable and we
cannot test its reliability or validity. Behavior, on the other hand,
can be observed and quantified by numerous observers, so Thorndike
turned toward
behaviorism
as a means for studying animal behavior.

Thorndike focused on the ability of animals to solve problems. In a well-known set of experiments, Thorndike placed cats in a variety of puzzle boxes,
and observed the strategies the cats used to escape. When first put in
a new box, the cats took a long time to find the solution, but after
becoming more experienced with the box, they were able to escape much
more quickly. Thorndike found that the cats improved their reaction
time by ignoring the ineffective actions and performing the useful
ones. This suggested to Thorndike that cats learn through trial and
error, and his conclusion helped to reinforce the belief that animal
behavior can be fully explained in associative terms.

While the psychologists succeeded in introducing much-needed rigor
into the study of animal minds, there was some concern that they had
gone too far, that the methods were too stringent, and that the drive
for repeatable and controlled experiments could not be used to uncover
all there is to know about the function of animal minds. For example,
the ethologists thought that in order to understand animal behavior,
animals must be observed in their natural environment. As sterile
laboratory experiments stripped the subjects of social and
environmental context, the worry arose that some studies may be
ecologically invalid.

With the fall of behaviorism and the rise of cognitivism in
psychology, animal cognition researchers have returned to
investigating animal minds. Today there are many approaches to
studying animals experimentally, in labs, zoos, dogs’ living rooms,
forests, fields, and oceans. One research program coming out of Kyoto
University’s Primate Research Institute (PRI) investigates chimpanzee
minds by combining captive experimental research with chimpanzees in
Kyoto and wild observational and experimental research with
chimpanzees in Africa (Matsuzawa et al. 2006). First, the
physical, cognitive, and social development of chimpanzees is taken
into account in the design of experiments, and subjects are raised by
their mothers rather than by human caregivers or unrelated animals. In
addition, lab work and fieldwork is synthesized; field observations
are used to develop experiments, and experiments are conducted both in
the field and in the laboratory. Finally, the method includes analysis
of the physiological and biological features of the species that could
be related to cognitive abilities.

The experimental research at PRI uses what they call the
“participant observation” method, which is based on the
triadic social relationship between mother, infant, and experimenter.
When testing chimpanzees in the lab, they are never taken from their
natural social environment; rather, the experiments are brought into
the social environment. As a researcher becomes a member of that
social environment, she can run experiments that are woven into normal
daily activities. At PRI, a different researcher is bonded with each
mother-infant dyad, and the relationship is expected to last a
lifetime. This close relationship between human and chimpanzee is
thought to offer many benefits. It makes the chimpanzees more willing
to engage in the research activities, so the researcher can gain a
better understanding of what chimpanzees can and cannot do (rather
than what they are willing or not willing to do). In addition,
Matsuzawa claims that the participant observation method fares better
at investigating species-typical social cognition than isolated
experiments on single subjects, because the PRI chimpanzees are not
integrated into a human social environment, but the researchers
themselves adapt to the chimpanzee social environment (Matsuzawa
2006a). Finally, the bond between researcher and subject allows the
human to interact with his chimpanzee “research partners”
at a younger age, given the relation of trust between researcher and
mother. Mother and infant can be taught a task together, which can
help to illuminate developmental differences in particular abilities.
For example, Inoue and Matsuzawa reported that infant chimpanzees are
better able to recall strings of numerals in order than adult
chimpanzees and humans (Inoue & Matsuzawa 2007).

Another way to avoid the worry about the ecological validity of
experiments is to perform them in the animal’s natural environment. In
field experiments, researchers intervene in the natural environments
of their subjects, rather than in a laboratory. One kind of field
experiment is the playback experiment, in which researchers play an
audio recording of an animal call in order to test the behavioral
responses to the call in various contexts. Cheney and Seyfarth
describe the structure of playback experiments as follows: researchers
first collect recordings of various calls given by different
individuals. Then, they examine the responses of the subject when a
recording of some individual’s call is amplified by a hidden
loudspeaker (the individual whose call is being played is generally
out of sight in such experiments). The subject is video recorded
before and after the playback. These experiments can be used to test
how the same subject responds to two different calls played in the
same context, as well as how the subject responds to the same call
played in different contexts (Cheney & Seyfarth 2007). Playback
experiments have been used in many contexts across species; for
example, such experiments have shown that vervet monkeys respond
differently to different alarm calls given for different predators
(Seyfarth et al. 1980), that baboons grunt to reconcile after a
fight (Cheney et al. 1995), and that baboons reconcile after a
relative of the aggressor grunts (Wittig et al. 2007a).
Playback experiments also suggest that bottlenose dolphins recognize
the signature whistle of their relatives (Sayigh et al. 1998;
Janik et al 2006), and that great male tits differ on personality
dimensions (Amy et al. 2010).

3.3 Observational Methods

While early psychologists were performing animal behavior experiments
in the laboratory, ethologists such as Oskar Heinroth, Konrad Lorenz,
Nikolaas Tinbergen, and Karl von Frisch were following animals into
the field to observe naturally occurring behavior. The ethological
method comes from biology, and takes into account not only the
behavior, but also the context of behavior, the environment, and the
physiology and evolutionary history of the animal. However, at least
initially, the ethologists were less interested in cognitive
mechanisms than were the experimentalists. Classical ethology gave
birth to a number of different fields of study. Behavioral ecology
developed into a unique field oriented towards determining how a
behavior evolved and the functions of that behavior (Krebs & Daies
1993; Wilson 1975). Cognitive ethology was born as a field focused on
animal consciousness (Griffin 1984, 1985, 1992), though in this
literature consciousness is as much of a focus as beliefs, intentions,
self-awareness, deception, and theory of mind. Griffin’s use of the
word “consciousness” belies his greater interest in
cognition, given that most of the topics he discusses (e.g.
perception, memory, spatial cognition, language, tool use etc.) are
cognitive (Griffin 1992). Today few researchers welcome the cognitive
ethology label (Allen 2004; Shettleworth 2010a). Rather, we witness an
integration of the experimental and ethological approaches to studying
cognition by scientists in psychology, biology, and anthropology,
among other fields (Shettleworth 2010a). Some now use the term
“cognitive ecology” to refer to the study of cognitive
processes in an animals’ natural environment (Healy & Braithwaite
2000; Real 1993).

In the early days of ethology, Lorenz and Tinbergen were interested in
analyzing the complex and rigid set of movements that make up a single
act. They postulated that such movements, which they called fixed
action patterns, are innate and caused by the existence of a releasing
mechanism that responds to some external sensory stimulus. Ethologists
study such acts at various organizational levels, e.g. at the
individual, dyad, family group, and species levels (Menzel 1969). To
explain behavior, ethologists follow Tinbergen’s suggestion that we
can distinguish between explanations in terms of proximate
causes, such as mechanism and function, and ultimate
causes, such as ontogeny (the maturational processes involved in
the behavior) and evolution (Tinbergen 1963).

But before an explanation for some behavior can be found, the behavior
must be well understood in the context of species-normal behavior.
Thus, the ethologist will begin the study of a species by constructing
an ethogram from field notes taken after many hours of observation
(Brown 1975; Lehner 1996). An ethogram is a thorough catalogue of the
characteristic behavioral units of a species, and each unit is given a
verbal description and perhaps an image. Theoretical debates arise
over the issue of how an ethogram should label and describe behavioral
units. Behaviors can be described formally, and describe the action at
the level of muscular contractions, or patterns of bodily movements
(e.g. beak pecks ground). On the other hand, an ethogram could
describe behaviors functionally, and place the behavior in a larger
context by referring to the purpose or consequence of the behavior
(e.g. eats) (Hinde 1970).

There are criticisms of both functional and formal methods of
description. Formal descriptions may leave out important aspects of an
animal’s behavior, whereas functional descriptions are subject to
over-interpretation and may lead to anthropomorphism, and they may
conflate explanations in terms of ultimate and proximate causes (see
Allen & Bekoff 1997 for a discussion). Millikan (1993) and Allen
and Bekoff (1997) provide philosophical defenses of relying on
functional descriptions in ethology. While Millikan has claimed that
ethologists should only be concerned with behavior as functionally
described, Allen and Bekoff argue that the choice between a functional
and formal description will vary on the context, depending on which is
more useful. In many cases, functional descriptions will be preferred
because of the advantages Hinde (1970) has identified. For one,
behavior described functionally will result in fewer data sets, making
for more robust data analysis. In addition, descriptions in terms of
function are more informative than formal ones, given that they
include information about the cause of the behavior or its
consequence. Finally, behavioral changes can be described in terms of
environmental changes.

However an ethologist decides to describe behaviors, the question of
how to individuate a behavior arises (Russon et al. 2007;
Skinner 1935). Descriptions of behavior can be finely grained, and
refer to the specifics of a behavior, e.g. using a stone (or a leaf
cup, or chewed leaves, or a hand, or fur, etc.) for drinking water
from a river. On the other hand, behaviors can be roughly grained into
larger behavioral units, e.g. using a drinking tool. If behaviors
should be categorized to reflect the way the species organizes its
behavior, then identifying behaviors requires first knowing the
species’ internal organizational scheme (Byrne 1999; Russon et
al. 2007).

While ethograms are used to record typical behaviors, many researchers
also collect rare and nonstandard behaviors, as well as behaviors that
just have not been observed before. These reports are often referred
to as “incidents” or “qualitative reports”
rather than “anecdotes”, in order to avoid the negative
connotation associated with the anecdotal method of Darwin’s
contemporaries. Researchers publish incidents when they indicate that
a species engages in previously unknown behaviors. For example, when
Jane Goodall reported seeing chimpanzee hunting and lethal intergroup
aggression, the scientific image of chimpanzees had to be
significantly revised (Goodall 1986). An example of a more recent
observational finding suggests that juvenile chimpanzees may play with
sticks in the way human children play with dolls (Kahlenberg &
Wrangham 2010).

The psychologist Richard Byrne defends the scientific use of rare
events as a useful research tool, writing that “careful and
unbiased recording of unanticipated or rare events, followed by
collation and an attempt at systematic analysis, cannot be harmful. At
worst, the exercise will be superseded and made redundant by methods
that give greater control; at best, the collated data may become
important to theory” (Byrne 1997, 135). By sharing their
incident reports, researchers have engaged in collaborative research
projects to study rare or unusual behaviors, such as deception (Whiten
& Byrne 1988) and innovation (van Schaik et al. 2006), and
systematic study often begins with observations of spontaneous
behavior (Bates & Byrne 2006; Russon & Andrews 2010, 2011; van
Schaik et al. 2006).

4. Research Programs

The various research programs in animal cognition can all be seen as
an attempt to uncover the underlying mechanisms involved in behavior.
The functional approach aims to categorize behavior functionally
(according to either ultimate or proximate function), and to discover
the cognitive mechanism(s) that are use in such behaviors. The initial
problem, of course, is to know whether the behavior counts as having a
particular function; this is particularly troublesome when a certain
function has a mechanism closely identified with it. For example,
while communication understood by a biologist in terms of ultimate
function is nothing more than an adaptive process of information
exchange between two individuals that does not require cognition, the
use of the term to describe a behavior may have Gricean implications
to others, which in turn can lead to unjustified inferences about the
about mechanism.

Animal behavior has been long explained in terms of what are sometimes
thought to be “simple” associative learning methods such
as classical and instrumental conditioning, or in terms of
species-specific responses. Species-specific responses are what are
sometimes called innate behaviors, but this term is problematic for a
number of reasons (Bateson and Mameli 2007; Mameli & Bateson
2011), and has largely been abandoned by scientists; they refer
instead to the predispositions demonstrated by typical members of the
species or subset of species (e.g. the long call of the male
orangutan). Associative learning comes about through experiencing
relationships between events, and results in an expectation that the
relationship will continue. However, the more we learn about the
processes involved in building associations, and the more it seems we
can do with them, the less “simple” and the more cognitive
they appear to be (Carruthers 2009, De Wit & Dickinson 2009;
Dickinson 2009; Rescorla 1988; Shettleworth 2010a). Other processes
thought to be involved in some instances of animal behavior are
transitive inference, causal learning, and path-integration. Questions
arise about the nature and cognitive requirements of these processes,
and their relation to associative learning.

The research programs in animal cognition are too numerous to
thoroughly cover here; fortunately, there are good introductory
psychology texts (e.g. Shettleworth 2010a; Wynne & Udell 2013) and
biology texts (Dugatkin 2013). What follows is a brief introduction to
some areas of research that have been of interest to philosophers.

4.1 Communication

Animal communication is often described as information exchange
between a sender who signals a receiver. Biological approaches
consider communication to occur when one animal’s behavior serves as a
stimulus that causes a change in another animal’s behavior, regardless
of whether the signaler’s behavior is intentional or voluntary; for
example, a female chimpanzee’s genital swelling that is observed by a
male who initiates a courtship is an instance of communication. In
biology contexts, this is often referred to as “functional
communication”.

For a discussion of contemporary theory and research in animal
communication, see Ulrich E. Stegmann’s collection Animal
Communication Theory: Information and Influence (2013).

4.1.1 Referential and Expressive Signals

Those who argue that animal communication systems and human language
are homologous (functionally similar due to common evolutionary
origin) or analogous (functionally similar with different evolutionary
origins) to one another have attempted to demonstrate that some animal
signals are referential in some sense. One test for referential
communication is to see if the behavior is flexible by determining
whether there is only a probabilistic relationship between the
stimulus and response. For example, if there are different responses
to different utterers (e.g. infant vs. adult, dominant vs.
submissive), this is thought to demonstrate flexibility in the
behavior that is suggestive of referential understanding (Evans 2002;
Tomasello & Zuberbühler 2002). In addition, it is thought
that referential calls will encode specific information about the
predator and that animals who hear the alarm call perceive that
encoded information (Evans et al. 1993a, Evans 1997).

Marler et al. (1992) offer two criteria that must be met for a
signal to be functionally referential. The production criterion
requires that all the stimuli that elicit the signal belong to one
category, either a general category such as “aerial
predators” or a more specific one such as “eagle.”
The perception criterion states that the utterance of the referential
signal is alone sufficient to elicit the same behavior as would be
elicited by perceiving the referent (Marler et al. 1992). Given
these criteria, Marler and Evans examined the anti-predator behavior
of bantam chickens, and found that the chickens reliably give
different alarm calls to aerial predators and ground predators (Evans
et al. 1993b; Evans & Marler 1995). Because they also
behave differently toward the two different predators, Marler and
Evans suggest that the alarm cries functionally refer to the kind of
predator approaching. When a chicken emits a scream after seeing a
hawk, they claim the chicken is referring to the hawk, and not simply
expressing fear of the hawk, or ordering conspecifics to take cover,
crouch, and look up to the sky.

Alarm calls and other communicative vocalizations that fulfill these
requirements are found in many species. Gunnison’s prairie dogs, for
example, give different alarm calls to humans, hawks, and
dogs/coyotes. In response to the hawk alarm call, only the prairie
dogs that are in the flight path of the hawk respond, running into a
burrow. The human alarm call elicits a community wide flight into the
burrows, whereas the dog/coyote alarm call leads all individuals to
run to the edge of the burrow and stand erect (Kiriazis &
Slobodchikoff 2006). Vervet monkeys also give alarm calls for
different predators. Following up on the field observations of
zoologist Thomas Struhsaker (1967), Cheney and Seyfarth used playbacks
of prerecorded alarm calls to demonstrate that when a leopard alarm is
sounded, the vervets run into trees, where they are safe from the
leopards due to the monkeys’ agility in jumping from tree to tree.
When an eagle alarm is sounded, monkeys look up and run into bushes.
When the snake alarm is sounded, the monkeys stand bipedally and peer
into the grass around them (Cheney & Seyfarth 1990). Other species
found to have different alarm calls and different behavior for
different species of predator include Diana monkeys (Zuberbühler
2000), Campbell’s monkeys (Zuberbühler 2001), and meerkats
(Manser 2001; Manser et al. 2001). In addition, ground
squirrels (Owings & Hennessy 1984), tree squirrels (Green &
Meagher 1998), and dwarf mongooses (Beynon & Rasa 1989) are all
known to have alarm calls that distinguish between terrestrial and
aerial predators. Many other calls and gestures are thought to involve
referential communication of this sort. For example, the food calls of
chimpanzees are thought to indicate not only the presence of food, but
also the location or quality of food (e.g. Slocombe &
Zuberbühler 2005, 2006). Similar findings have been reported for
chickens (Evans & Evans 2007). Bottlenose dolphins are said to
refer to themselves via their signature whistle (Janik et al.
2006).

In Darwin’s The Expression of Emotions in Man and Animals, he
argues that animal signals are expressions of emotions with the
function of relaying the signaler’s emotional or motivational states.
These signals are largely species-specific and not flexibly used. For
example, Darwin describes the cat’s arching and hissing at a dog as an
expression of the cat’s terror and anger (Darwin 1886, p. 56).
Ethologists followed Darwin’s lead in describing animal signals as
expressions of emotional or motivational state, and many ethologists
thought that expressive signals were incompatible with referential
ones. The assumption that animal signals are expressions of emotions
led scientists to focus on questions such as whether animals
communicate degree of arousal or motivation — emotional
expression in analog and not just in binary, and many studies suggest
that some do. However, since the 1980’s there is a growing body of
evidence that undermines the assumption that there is a dichotomy
between expressive signals and referential ones, and some argue that
animal signals can inform receivers about both motivational state and
external objects or events (Marler et al. 1992; Manser et
al. 2002). For example, we can see evidence for this claim from
the research on suricate alarm calls. Suricates are a variety of
African mongoose known to give different alarm calls to mammalian,
avian, and reptilian predators, and they respond differently to the
calls depending on the degree of urgency the call demonstrates (Manser
et al. 2001). In playback experiments, suricates respond qualitatively
differently to the three different alarm calls, and they respond
quantitatively differently to different levels of urgency within each
alarm call class. For example, in response to a low urgency snake
alarm recording, suricates will raise their tails, approach the
loudspeaker, and sniff the area around it, but they will quickly
resume their previous activity. However, if a high urgency alarm call
is played, the suricates will continue the alarm response behavior for
a significantly longer time.

While it is clear that human language can simultaneously refer to
external events and express an individual’s feelings about those
events (e.g. “Fire!” vs. “Fire?”), questions
remain about the nature of reference in animal communication, and
whether it is the same or different from how linguistic expressions
refer. To describe a call as functionally referential is to remain
agnostic about the role of cognition. Moreover, this leaves aside the
question of whether the act is voluntary, intentional, or involves
mental representations. Philosophical questions can be raised about
the referential nature of animal signals in terms of their
meaning,
truth-evaluability, and the mechanisms involved. One philosophical
treatment of the nature of animal alarm calls defends the view that
alarm calls are best understood as neo-expressive avowals — self
reports of one’s present mental state that have both an action
component (the expressing) and a semantic component (the
representation) (McAninch et al. 2009). Allen and Saidel
suggest that empirical work can be done to determine the kinds of
referential communication different species can engage in, and that
such research can help us better understand the mechanisms subsuming
human language (Allen & Saidel 1998). For example, one might
investigate the claim that dolphin signature whistles function as
proper names in relation to various
theories of reference.

4.1.2 Intentional Signals

Most discussions of intentional signals in animal communication at
some point come into contact with the treatment by Daniel Dennett. In
his discussion of the possible meanings of vervet monkey alarm calls,
Dennett provides an analysis of the levels of intentionality that may
be at work (Dennett 1987). Dennett suggests that animal cognition
researchers would benefit from adopting the language of folk
psychology. Moreover, he claims that animals such as vervet monkeys
should be viewed as intentional systems whose behaviors are
predictable via attributing beliefs, desires, and rationality to them;
that is, Dennett suggests that scientists should talk about animal
beliefs (understood in terms of the intentional stance, of course).
The question, then, is what sort of beliefs do animals have when they
vocalize: do the vervets think about the effects of their calls on the
others’ behaviors, or do they think about the effects of their calls
on others’ epistemic states or minds?

Dennett’s view is inspired by his interpretation of H.P. Grice’s
theory of meaning. For Grice, a speaker means something by an
utterance x if and only if the speaker utters x with the intention
that: (1) it produces a response in the intended audience, (2) that
the audience recognizes the speaker’s first intention, and (3) that
the audience’s recognition of the speaker’s first intention serves
as a reason for the audience responding as it does (Grice 1957). Given
Grice’s requirement, Dennett suggests only creatures who can hold a
third-order belief (e.g., I think that she thinks that I think) will
be communicators (Dennett 1987). That would entail that only creatures
who mindread, that is, those who can think about others’ mental
states, are able to communicate. This leaves out some cognitively
diverse human adults, human infants and probably most animals.

While strong criteria for intentional communication requires
third-order intentionality, lower bars can be set (Gómez 2007; Moore
2014, 2015; Sperber& Wilson 1986). Perhaps the most basic
requirement for a communicative signal to be intentional is for it to
be under the signaler’s voluntary control. In addition to
neurobiological evidence that some animal signals are voluntary (Allen
& Saidel 1998; see also articles in Platt & Ghazanfar 2010),
there is behavioral evidence that animal signals can be used flexibly
in ways that suggest voluntariness. Audience effects are one example
of the flexibility that characterizes some signals. Roosters, for
example, will alarm call more frequently in the presence of a hen than
when they are alone (Evans & Marler 1995), and in the presence of
a conspecific than in the presence of a bobwhite quail (Karakashian
et al. 1988). Another example of apparent flexibility in
controlling vocalizations comes from observations of chimpanzee border
patrols, an extremely risky practice that may result in death
following an intergroup encounter. As chimpanzees approach the border
of a neighboring community, they become unusually silent (Mitani et
al. 2002).

There is also behavioral evidence that animal signals may be context
sensitive in the same way linguistic utterances are. Just as some
argue that the pragmatic context of language does significant semantic
work for humans (e.g. Stalnaker 1999; Sperber and Wilson 1986), both
the production of and the responses to animal signals can be dependent
on contextual factors. For example, while honeybee waggle dances
communicate to the nest mates the location of a food source, the dance
does not determine their response; if an individual remembers the
location of a preferable food source, the bee may visit that source
instead (Grüter et al. 2008; Grüter & Farina
2009). And, on the production side, there is experimental evidence
that orangutans repeat a gesture when they are only partially
understood, and will use different gestures when a message is
misunderstood (Cartmill & Byrne 2007).

Michael Tomasello argues that some ape signals are examples of
intentional communication, and claims that intentional signals “are
chosen and produced by individual organisms flexibly and strategically
for particular social goals, adjusted in various ways for particular
circumstances. These signals are intentional in the sense that the
individual controls their use flexibly toward the goal of influencing
others” (Tomasello 2008, 14). To make an intentional signal,
Tomasello thinks one must be able to flexibly use the signal, be aware
of the attentional state of the communicative partner, and be able to
learn the signal.

While much of the interest in animal communication has been focused on
vocalizations, some believe that gestural communication may be a
better place to look for intentional communication, especially in the
great apes and especially if gestural theories of language evolution
are correct (e.g. Corballis 2002; Arbib, 2002; Arbib et al.,
2008; Pollick & de Waal 2007). Gestural communication has been
studied in gorillas (Genty et al. 2009), orangutans (Cartmill
& Byrne 2007; Russon & Andrews 2010), and chimpanzees
(Tomasello 1994; Pika & Mitani 2006; Pika et al. 2005).
Recent reports claim that orangutans also use sequences of gestures to
communicate requests and other messages by acting out, or pantomiming,
what they intend to communicate. For example, in the context of a
head-cleaning game, a semi-free ranging young orangutan was observed
to take a leaf from a human’s hand when the human refused to use it to
clean his head. The orangutan then briefly rubbed his head with the
leaf, and then handed the leaf back to the human, who proceeded to
clean the orangutan’s head with it (Russon & Andrews 2010).
Unsystematic reports of pantomime behavior can be found for other
great apes as well, though formal research programs on pantomime in
great apes need to be pursued further (Russon & Andrews 2011).

4.1.3 Symbolic Communication

In the 20th century there was great interest in teaching
symbolic communication systems to other species. The earliest forays
into this area were with chimpanzees, and focused on teaching spoken
language to chimpanzees raised as human children (Kellogg &
Kellogg 1933; Hayes & Hayes 1951). With the realization that
chimpanzees lack the vocal apparatus needed to utter human words,
research shifted to teaching chimpanzees American Sign Language and
artificial symbolic communication systems. The first such study,
Beatrix and Allen Gardner’s Project Washoe, was initially reported to
be a success. Using explicit training methods, including shaping,
molding, and modeling, the researchers were able to train the infant
Washoe to form at least 132 ASL signs. Focus was on production of
gestures, rather than comprehension, and the Gardners’ stated
intention was to train Washoe (and later, other chimpanzees) in a
social setting, mimicking the language-learning environment of
children as much as possible. The Gardners claim that, “[Washoe]
learned a natural human language and her early utterances were highly
similar to, perhaps indistinguishable from, the early utterances of
human children. Now, the categorical question, can a nonhuman being
use a human language, must be replaced by quantitative questions: how
much human language, how soon, or how far can they go?” (Gardner
& Gardner 1978, 73).

While the Gardners’ claims about ape language were being echoed by
others working on ape language (e.g., Premack 1971; Patterson 1978),
not everyone agreed. The psychologist Herbert Terrace, who used the
methods of the Gardners to teach ASL signs to an infant chimpanzee
named Nim Chimpsky, argued that the apes were not using the signs to
communicate. Terrace concluded that some of the results achieved by
the Gardners could be explained by associative learning rather than
comprehension of the semantics of the symbols. In his study, he tried
to control for associative learning, and his focus on syntax had him
attend to symbol order in multi-symbol strings. While early results of
this study seemed promising, after watching videos of Nim’s symbol use
he noticed that what had been initially seen as spontaneous utterances
were often imitations of utterances just made by his trainers. Terrace
reviewed films of Washoe’s utterances, and found similar patterns: the
teacher initiates the signing, and the chimpanzee mimics the teacher’s
signs. He also noted that the give and take rhythm of child-adult
communication was not mirrored by the chimpanzee-trainer
conversations, and took this difference in pragmatics as further
evidence that the chimpanzees were not using language (Terrace et
al. 1979).

Though the Gardners defended their studies against Terrace’s critiques
(Gardner & Gardner 1989), other researchers tried to control for
alternative interpretations of their results. Premack, for example,
relied on transfer tests as evidence that the chimpanzee Sara
understands the symbols she was taught (Premack 1971). In a transfer
test, a new symbol is taught only in the context of a subset of the
subject’s vocabulary. Once the subject reaches criterion on the
teaching set, a formal test is conducted using novel strings of
symbols.

The post-Terrace research on symbolic communication expanded to
include different species, such as the other apes, dolphins, parrots,
and sea lions. In addition, the focus of some studies has shifted from
syntax to semantics, and from production to comprehension. More recently, the investigation into whether other species can learn a symbol system in order to communicate with humans has largely lapsed, with focus shifting back to investigating syntactic skills independently from communicative meaning, and to animals’ natural communication capacities.

Symbolic Communication Research

Species

Study

Description

Chimpanzee

Kellogg & Kellogg (1933)

Co-rearing of a 7 1/2 month-old female chimpanzee,
Gua, with their 10 month-old son, Donald, for nine months. Both were
explicitly trained in spoken English. Though Gua failed to produce
language, she was said to comprehend 95 terms by the end of the
study.

Chimpanzee

Hayes & Hayes (1951,1952)

A female chimpanzee Vicky was raised from infancy
as a human child for almost 8 years. Despite extensive training, Vicky
was only able to utter four words.

Chimpanzee

Gardner & Gardner (1971)

Explicit teaching of ASL signs to a female
chimpanzee Washoe in a social setting. Washoe was 11 months-old when
the project started, and after 51 months of training she reached
criterion on 132 signs.

Chimpanzee

Premack (1971)

Explicit teaching of symbol use to a 6 year-old
chimpanzee Sarah in a laboratory setting. Sarah was taught to
associate objects, actions, classes, logical connectives, etc. with
plastic chips, and was taught to produce strings of symbols that obey
syntactic rules.

Chimpanzee

Rumbaugh (1973)

Explicit teaching of a lexigram system to 2 1/2
year-old female chimpanzee Lana in a computer-mediated laboratory
setting. Lana produced strings of lexigrams that obey syntactic rules.
Later Lana’s performance was said to be an emulation of human symbol
use because she failed to grasp the referential aspect of the
lexigrams.

Chimpanzee

Savage-Rumbaugh (1980)

Explicit teaching of a lexigram system to two male
chimpanzees, Sherman (5 years-old) and Austin (4 years-old). Emphasis
was on semantics rather than syntax. Sherman and Austin were reported
to use the lexigrams with one another to request objects.

Gorilla

Patterson (1978)

Explicit teaching of ASL signs to a female gorilla
Koko in a social setting.

Chimpanzee

Terrace et al. (1979)

Explicit teaching of ASL signs to a 2 week-old
male chimpanzee, Nim Chimpsky, using the methods of Gardner &
Gardner; failed to replicate their results.

Orangutan

Miles (1983)

Explicit teaching of ASL to an encultured male
orangutan Chantek in a social setting. Chantek was 9 months-old when
the project started, and it continues nearly twenty years later.

Sea lion

Schusterman et al. (1984)

Explicit teaching of comprehension of an
artificial gestural communication system to a female sea lion, Rocky,
since 1978, modeled after the bottlenose dolphin communication system
developed by Lou Herman.

Chimpanzee

Matsuzawa (1985)

Explicit teaching of numeral use to a female
chimpanzee Ai in a social setting. Ai was 1 year-old when she arrived
at Kyoto in 1977. Research continues on the language, numerical, and
other cognitive abilities of chimpanzees, including developmental
studies of Ai’s son, Ayumu, using the participant observation
method.

Bonobo

Savage-Rumbaugh (1986)

Spontaneous acquisition of lexigram symbol use in
a 2 1/2 year-old male bonobo Kanzi after almost two years of observing
explicit attempt to teach his surrogate mother.

Bottlenose dolphin

Herman et al. (1986)

Explicit teaching of comprehension of an
artificial gestural communication system with some logical structure
to four captive dolphins, Phoenix, Akeakamai, Hiapo, and Elele.

Chimpanzee

Fouts et al. (1989)

Social learning of ASL from a trained chimpanzee
Washoe to a young naive chimpanzee Loulis.

Chimpanzee

Boysen & Berntson (1989)

Explicit teaching of numerals to an encultured
female chimpanzee, Sheba.

African Grey Parrot

Pepperberg (1999)

Social modeling of spoken language used to teach
the parrot Alex to vocalize English words. Alex was able to label
objects by name, color, shape, and matter.

Orangutan

Shumaker (1997)

Explicit teaching of a symbolic lexigram
communication system with some logical structure to the male orangutan
Azy, ongoing since 1995.

Border Collie

Pilley & Reid (2011)

Explicit teaching of unique proper-name nouns
(spoken in English) for 1022 objects to Chaser, whose training began
by her caregivers when she was 8 weeks old.

Advocates of this research program argue that the studies uncover
something about the relationship between language and mind, the
evolution of human language, and the roles played by development and
scaffolding in human language (Lloyd 2004). However, to the critics,
these studies simply provide more evidence in favor of the power of
association and the ability of humans to train animals to do nearly
anything. There is a huge literature on these studies, with critics
(Pinker & Bloom 1990; Pinker 1994; Chomsky 1980) as well as
defenders (Lloyd 2004; Greenfield 1991; Savage Rumbaugh et al.
1998).

One area of contention has to do with whether animals that
successfully use some aspect of human language are using it qua
language, or are instead engaged in symbolic communication. At least
three different demarcations between language and other symbolic
communication systems have been offered. According to Noam Chomsky’s
original linguistic program, to use language is to embody certain
structural principles, and all language users are able to produce a
potentially infinite number of grammatical strings via recursive
embedding (Chomsky 1968). The linguistic anthropologist Charles
Hockett identified up to seventeen design features that occur in every
human language, including semanticity, discreteness, and arbitrariness
(Hockett 1977). More recently Hauser, Fitch, and Chomsky (2002) argue
that the mechanism that allows for recursive thinking is the central
cognitive requirement for language, and is a feature of human
communication systems not found in other species. However, research on European starlings finds that we can train birds to discriminate a recursive grammar from among strings of starling sounds (Gentner et al. 2006), and that there are similarities between human language and birdsong along cognitive, neurological, genomic, and behavioral dimensions (Bolhuis, J.J. et al. 2010).

Chomsky was a vocal critic of early animal language studies,
especially of the claims made by some researchers that the apes had
acquired language. For Chomsky, language requires syntax, which he
claimed was lacking in all the communication systems of the apes.
Furthermore, to train an ape to use symbols is a laborious process,
whereas children learn language effortlessly. Language is innate,
according to Chomsky, so if apes had the capacity for learning
language, they would speak without human intervention (Chomsky 1968).
Chomsky often states his criticism as an a priori argument against
animal language: : “if an animal had a capacity as biologically
sophisticated as language but somehow hadn’t used it until now, it
would be an evolutionary miracle, like finding an island of humans who
could be taught to fly” (cited in Lloyd 2004, 585).

Another argument Chomsky has offered against animal language is based
on the dissimilarity between animal communication systems and human
language. He writes, “The question of whether other systems are
‘like’ human language is a question about the usefulness of a certain
metaphor” (Chomsky 1980, 434), and he argues that the structural
principles, manner of use, and ontogenetic development of ape symbol
use is so different from human language that any analogy between the
two would be very weak. Those who defend the animal symbolic
communication system as language take Chomsky to task on this point,
and stress the similarities between the two systems of
communication.

Given findings in genetics, the biological capacity for language may
be more accurately described as a collection of biological capacities,
some of which we share with other species. The FOXP2 gene is found to
play a role in speech production, and some claim that it was
instrumental in the development of language in humans. The FOXP2 gene
is also expressed in the same part of the brain in zebra finches, and
it has been reported that finch fledglings with reduced FOXP2 are
impaired in their ability to learn to sing (Haesler et al.
2007).

4.1.4 Gestural communication

While most discussions of animal communication focus on vocal
communication, animal communication may also occur through different
modalities. The bee dance is an example of a postural method of
communication. Great apes communicate using gestures, such as
pantomime (Russon & Andrews 2011). The idea that human language
evolved from body movements such as gesture, miming, and dance has
been promoted by Michael Corballis (1992, 2002) and Merlin Donald
(1991) as the gestural theory of language acquisition. Because for
primates bodily movement is under voluntary cortical control to a
greater extent than are vocalizations, it may be that our hominid
ancestors, as well as our great ape cousins, use gesture and posture
to intentionally communicate. The neuroscientist Michal Arbib supports
the gestural theory of language evolution, through appeal to the
mirror system, which is a neural system found in humans and other
primates which is active both when witnessing another engage in an
action and when one engages in that action oneself (Arbib 2005).

Tomasello claims that chimpanzees intentionally communicate only via
gestures, because when gesturing but not when vocalizing apes monitor
the gaze of communicative partners (Leavens and Hopkins 1998), and
repair failed communication attempts by repeating a message or
elaborating on it (Liebal et al 2004, Leavens et al 2005).

Ravens have been observed to use gestural communication, in their head
and beak movements that indicate the presence of objects such as moss
or twigs to their partner (Pika & Bugnyar 2011). Elephants have
also been observed to use postural communication, as when they orient
their body to indicate where they want to go next (Poole & Granli
2011). In addition, some elephants understand human pointing (Smet
& Byrne 2013).

4.2 Mindreading or Theory of Mind

Like humans, many species are social animals who, in addition to
navigating a physical world, must also navigate a social world. In the
1970s it was suggested that in order to succeed in a competitive
social world, individuals would benefit from having some understanding
of the mind of others (Humphrey 1976, 1978), with Alison Jolly
suggesting that knowing other minds helps members of big social groups
cooperate (Jolly 1966). Humphrey wrote, “...I venture to suggest
that if a rat’s knowledge of the behavior of other rats were to be
limited to everything which behaviorists have discovered about rats to
date, the rat would show so little understanding of its fellows that
it would bungle disastrously every social interaction it engaged in;
the prospects for a man similarly constrained would be still more
dismal” (Humphrey 1978, 60). The idea here is that knowledge of
other minds can offer added value over knowing others’ behavioral
patterns.

The term “theory of mind” was introduced by psychologists
David Premack and Guy Woodruff around this time. The specific question
Premack and Woodruff were interested in was whether the chimpanzee
attributes beliefs and desires in order to predict and explain
behavior, something they assumed that humans do. In effect, Premack
and Woodruff wanted to know whether a chimpanzee is a Humean action
theorist who understands the behavior of others as being caused by
propositional attitudes. Thus, while they defined theory of mind as
the ability to predict and explain behavior by attributing mental
states, they were more focused on whether chimpanzees engage in belief
and desire reasoning. Premack and Woodruff attempted to determine
whether Sarah, the same chimpanzee from Premack’s symbolic
communication project, has a theory of mind. To examine whether Sarah
understands what others believe, they used the following paradigm:
Sarah was shown videotapes of humans trying to solve certain tasks
(e.g. acquiring out of reach bananas, warming up a cold room by
lighting a heater) and she was supposed to choose from an array of
photos to pick the solution (Premack & Woodruff 1978). Because
Sarah picked the correct photograph at an above-chance level, Premack
and Woodruff concluded that she has a theory of mind. They claimed
that Sarah must have been attributing “at least two states of
mind to the human actor, namely, intention or purpose on the one hand,
and knowledge or belief on the other” (Premack & Woodruff
1978, 518).

In commentary on this study, it was pointed out that Sarah could have
used other methods to solve the problems. She could, for example, have
attended to the goal of the actors, as opposed to their mental states
(which is the interpretation that Premack now endorses (Premack &
Premack 2003)). Most of the commentators were unconvinced by the
design of the study, and several suggested alternative methodologies
for examining the question. One suggestion was to require the subject
to solve a coordination problem. In order to succeed in a coordination
problem, the subject would have to alter his own behavior in
expectation of what another will do (e.g. Bennett 1978; Dennett 1978;
Harman 1978). Dennett suggests that a good coordination problem might
require that the subject considers another’s false belief, so that the
behavior being predicted will be an unusual one, such as a behavior
that would only be exhibited if the actor had a false belief. A false
belief coordination problem would thus avoid alternative
interpretations having to do with identifying the actor’s goal, or
making associations from similar situations in the past. The behavior
performed by an actor who has a false belief will not achieve the
actor’s goal, and will probably not be something the subject has
witnessed previously. The main problem with this suggestion, Dennett
notes, is how to determine the content of the predictions a chimpanzee
might make.

Given the difficulties associated with developing a good nonverbal
test for mindreading (Dennett 1983), Dennett’s suggestion was taken up
by researchers interested in studying theory of mind in children
(Wimmer & Perner 1983). Wimmer and Perner accepted Premack and
Woodruff’s definition of theory of mind, and become interested in the
question of the stage at which small children acquire a theory of
mind. To answer this question, they designed the false belief
task, which was to become a standard test for theory of mind.
Children watched a show in which a puppet named Maxi puts away a piece
of chocolate in a box before leaving the room. While Maxi is out, his
mother finds the chocolate and moves it to a cupboard. Maxi returns to
the scene, the show is stopped, and children are asked to predict
where Maxi will go to look for his chocolate. If the child says Maxi
will look in the cupboard, she fails the test, and thus shows that she
doesn’t have a theory of mind. If the child says Maxi will look in the
box, she passes; passing the task shows that the child has a theory of
mind, because she demonstrates that she can attribute mental states
and use them to predict Maxi’s behavior.

This research program was closely associated with a debate on folk
psychology between
folk psychology as theory
(the view that human knowledge of other minds is theoretical in
nature), and
folk psychology as simulation
(the view that our knowledge of other minds relies on using our own
mind as a model). Due to this debate, philosophers starting
substituting the term "mindreading" for the term "theory of mind" so
as to be inclusive. In the late 1990s there was a growing acceptance
that both theory-theory and simulation theory were partially right and
partially wrong, and this culminated in a general acceptance of some
sort of hybrid theory (e.g. Nichols & Stich 2003; Goldman 2006).
These arguments make use of empirical data from both the developmental
and the animal cognition literature.

During this time, there were a few attempts to uncover mindreading in
animals using nonverbal paradigms, without much success (Heyes 1998).
Given the subsequent theoretical and definitional disagreements, some
researchers have concluded that “the generic label ‘theory of
mind’ actually covers a wide range of processes of social
cognition” (Tomasello et al. 2003b, 239). The research
program in animal mindreading subsequently shifted from attempts to
come up with a nonverbal false belief task, toward more specific
questions about cognitive capacities like understanding others’
perceptual states (Hare et al. 2000), goals (Uller 2004), or
intentionality (Tomasello 2005).

4.2.1 Mindreading perceptions

There has been much interest in examining primates’ understanding of
perceptual states. The standard approach in the investigation starts
with two assumptions: that perceptual states, like belief states, are
hidden from the observer’s standpoint and hence have to be postulated
as theoretical entities; and that knowing someone’s perceptual states,
like knowing another’s belief state, facilitates accurate predictions
about future behavior. Ethological evidence that chimpanzees monitor
gaze and modify their behavior when they are visible to others (e.g.
Plooij 1978; Whiten & Byrne 1988; Goodall 1986) was taken to
provide evidence that chimpanzees can attribute perceptual states to
others. As a result, experimental researchers decided to design
studies meant to determine whether chimpanzees understand seeing.

The results of early laboratory studies were mixed; David Premack’s
research suggested that chimpanzees do understand seeing (reviewed in
Premack & Premack 2003), whereas studies by Povinelli and Eddy
(1996) challenged that conclusion. Later studies suggested that
chimpanzees understand both seeing and intentionality (Hare et
al. 2000; Hare et al. 2001). In Hare et al.’s
experimental set-up, a subordinate and a dominant chimpanzee are
released in a room baited with food. Normally, if both animals can see
the food, or see one another witness the baiting, a subordinate animal
will avoid the food and allow access to the dominant. However, in
these experiments, when the food is occluded from the dominant’s view,
the subordinate will approach it. Only if the dominant can see the
food or the baiting will the subordinate avoid it. The animals are
across the room from one another, so the subordinate has to consider
the visual perspective of the dominant in order to judge correctly
whether he can see the food or not. Because it seems that the
subordinate is able to make different judgments about whether to seek
out the food based only on whether it is visible to the dominant, this
study is thought to indicate that the apes understand the mental state
of seeing.

Povinelli and Vonk (2004) criticize the Hare et al. studies,
suggesting that the ecological nature of the study (using food
competition behavior from the subject’s natural repertoire) is a
weakness of the study, not a strength as the authors believed, because
the chimpanzees could have made inferences based on past observed
behavior. In response, the authors claim that they have accounted for
all possible alternative explanations for the subordinate’s behavior,
making an inference to the best explanation argument that the
subordinate understands what the dominant sees (Tomasello et
al. 2003a, 2003b; Hare et al. 2006).

In a review of the literature, Call and Tomasello (2008) conclude that
there is ample evidence that chimpanzees understand others’ goals,
intentions, perceptions and knowledge, but that there is no
experimental evidence that they understand false belief. Evidence for
this final claim comes from recent studies, such as a turn-taking food
competition study that suggests chimpanzees can understand other’s
knowledge state, but not their belief state (Kaminski et al.
2008). In this study, two chimpanzees take turns pointing at one of
three opaque buckets in order to gain food rewards that may be hidden
inside. In one condition, the subject chimpanzee observed two buckets
being bated, and the competitor chimpanzee only observed one bucket
being bated. When the subject chimpanzee was permitted to choose
first, the subject showed no preference for either of the baited
buckets. However, when the subject chimpanzee had to choose second
(without having the opportunity to observe the choice of the
competitor chimpanzee), the subject more often chose the bucket that
the competitor had not seen baited. The authors conclude that
chimpanzees sometimes know what others know. They then used a
variation of this experiment to test for false belief. The subject saw
the competitor chimpanzee being mislead about the actual location of
the food, but was not able to make use of this information to predict
the competitor’s first choice. This suggests that the chimpanzees were
unable to distinguish between others’ true and false beliefs. While
the authors claim, “These results suggest that, at least in some
situations, chimpanzees know what others know, in the sense of have
seen” (Kaminski et al. 2008, 229).

4.2.2 The logical problem and parsimony

Various worries have been raised about the design of the studies
testing for belief and perceptual attribution in great apes. Daniel
Povinelli and his colleagues have challenged the paradigms by arguing
that in each case the subjects’ performance can be explained by their
having a theory of behavior, rather than a theory of mind, and this
has been come to be known as "the logical problem" (Povinelli &
Vonk 2004). They claim that in all the studies that have been done so
far, chimpanzees could use a complementary behavior-reading rule S ->
B to predict behavior, rather than a mindreading rule S -> Ms -> B
(where S is the situational cue, B is the predicted behavior, and Ms
is the mental state). Povinelli and Vonk suggest that both humans and chimpanzees have a theory of behavior, but humans also have a theory of mind, in that being a mindreader requires also being a behavior reader.

One response to this challenge is to suggest that since mindreading
allows one to make predictions of behavior in novel situations, we can find evidence of mindreading when we see that a predictor makes a prediction from their personal experience, rather than from their experience observing others’ action. This ability to make novel predictions is the added value of mindreading over mere behavior-reading.
Taking this position, Povinelli and Vonk suggest that
Cecilia Heyes’ proposed experience projection paradigm would avoid the
logical problem, because it involves asking a chimpanzee to realize
that a novel situation (i.e. wearing a red bucket over the head) was
associated with a particular mental experience (i.e. not being able to
see). This test would require the subject make an inference not from
observed behavior to new behavior, but from introspected experience of
the self to the mental experience of the other. A chimpanzee who
passed this test would be able to predict that another chimpanzee who
wore the red bucket would not be able to see. That is, the chimpanzee would have to infer some intervening variable between observed behavior and action, and for Povinelli and Vonk this is sufficient evidence for mindreading (Buckner 2014). More recently, researchers have run versions of the goggles test on chimpanzees (Karg et al. 2015) and ravens (Bugnyar et al. 2016), and in both cases found that the animals are able to pass the task.

In the raven study, subjects are given
the experience of seeing into an adjoining room through a peephole,
and watching food being cached. They are then released into the room
at which point they retrieve the food. After having this experience,
researchers investigated the ravens’ caching behavior in the room
under three conditions: with a transparent window, without a
transparent window and no peephole, and without a transparent window
and a peephole. They found that ravens’ caching behavior was the same
in the transparent window and the peephole conditions, and
significantly different in the conditions without the transparent
window. The study authors conclude that this study provides evidence
of theory of mind in ravens (Bugnyar et al. 2016). In the chimpanzee study, subjects are exposed to food boxes having lids with different transparent properties, and chimpanzees are given an experience in which they learn that a lid that looks opaque is in fact transparent from another perspective. When chimpanzees compete for food with a human agent, they prefer taking food from opaque boxes over transparent boxes, and from boxes that are opaque over than boxes that appear to be opaque but which the subject had prior experience with, so they know that from the human agent’s view they are transparent (Karg et al. 2015). Given the findings from these two studies, it appears that the logical problem has been overcome.

However, there is a stronger interpretation of the logical problem which may not be met by those studies. On the stronger interpretation, there needs to be evidence that the intervening variable is properly mental, rather than behavioral (Lurz 2011). Raising a concern along these lines, Kristin Andrews (2005) argued that in the proposed goggles test the
chimpanzee can pass the test by understanding that the red bucket is
the bucket that hinders one’s ability to do things, rather than
the bucket that hinders one’s ability to see things; the
chimpanzee’s behavior is consistent with a behavior-reading rule as
well as a mindreading one. Since the prediction that the chimpanzee
participant would be asked to make would be about the other
chimpanzee’s behavior (such as begging from a person with food), the
chimpanzee participant may solve this task by realizing that the red
bucket hinders individuals’ abilities to achieve the specific goal. Rather than generalizing from one’s own mental experience, the successful chimpanzee subject may be generalizing from their own physical experience. Andrews goes on to argue that this stronger reading of the logical problem makes the issue of chimpanzee mindreading not one that is subject to scientific investigation, and turns the logical problem into the general skeptical problems of other minds (Andrews 2015).

In an attempt to avoid these sorts of alternative explanations (which Lurz calls "complementary behavior-reading hypotheses") Lurz
attempts to invent new research
paradigms for nonhuman animals, such that passing them wouldn’t be subject to a complementary behavior-reading hypothesis. He suggests that ape mindreading can
be directly tested using a version of the appearance-reality test.
Lurz points out that by considering the way an object appears to an
individual, one can better predict that individual’s future behavior
toward that object than by considering the actual properties of the
object. Following on this insight, he suggests that researchers ought
to examine whether chimpanzees take into consideration how objects
appear to others. In a review of this book, Andrews (2012b) argues
that Lurz’s proposed experiments also fail to avoid the logical
problem, as there are behavioral descriptions that can be offered for
successful performance on those tasks as well.

Povinelli and colleagues also suggest another added value to
mindreading is that the mental state can be used to reinterpret the
observed behavior. The Reinterpretation Hypothesis states that
representing others’ mental states has the primary function not of
predicting new behavior, but rather of providing a causal description
of behavior that can be predicted without appeal to mental states
(Povinelli et al. 2000). They write, "...the evolution of
second-order intentional states may have allowed humans to reinterpret
existing, extremely complicated social behaviors that evolved long
before we did...once this new representational device was in place,
there may well have been cascading effects on larger aspects of the
system — in this case, material and social culture including
pedagogy and ethics..." (Povinelli et al. 2000, 533). Given
this commitment, one may also examine the existence of these cascading
effects in other species in order to gain evidence of mindreading.
However, Povinelli is convinced that nonhuman great apes cannot reason
about unobservables, and do not understand causation (Povinelli &
Dunphy-Lelii 2001). This explains Povinelli and colleagues’ pessimism
that chimpanzees are mindreaders.

Rather than thinking a single experiment can serve as evidence for
chimpanzee mindreading, other philosophers have moved to considering a
larger body of evidence. Elliot Sober suggests that we can gain
evidence of a mentalistic intervening variable in animals by
conducting a two winged study using two different sorts of stimuli
that elicit two different types of behavior, which are unified for a
mentalizing subject but not for a behavior-reading subject. He asks us
to consider running a version of Melis et al.’s (2006) studies
in which chimpanzees are invited to steal food from humans in two
related conditions. In one condition chimps can reach through opaque
or transparent tubes to gain food, and in the other conditions chimps
can lift a noisy or silent trap door. Reaching through opaque tubes
and lifting quiet doors results in a successful theft. Sober argues
that according to the mindreading hypothesis passing one wing of this
task should increase the probability of passing the other wing of this
task, whereas for the behavior reading hypothesis there should be a
screening off of the two conditions, with no correlation between the
tasks (Sober 2015). Other objections to the logical problem challenge come from from Marta Halina (2015), who argues that the logical
problem is a form of skepticism that doesn’t enter into empirical
investigation. Fletcher
and Carruthers (2013) argue that the behavior reading hypothesis is
unfalsifiable.

The move to consider a body of research as evidence of mindreading is
sometimes couched in terms of parsimony (Whiten 1995). For example,
Logan Fletcher and Peter Carruthers (2013) review the evidence for
successes and failures in chimpanzee mindreading tasks, and, appealing
to considerations of parsimony, conclude that the evidence suggests
that apes mindread desires, perceptions, and knowledge. The hypothesis
that chimpanzees mindread unified a large body of evidence. However,
considerations of parsimony have been used both to conclude that
animals do, and to conclude that animals do not, mindread. This had led to an
investigation that focuses on the role of parsimony in the mindreading
debates. Sober investigates both evolutionary parsimony and black box
parsimony, and offers suggestions about the kind of evidence we need
to move ahead in the debate (2015). Other investigations into the role
of parsimony in investigating animal mindreading comes from Hayley
Clatterbuck (2015), who argues that it is simpler for chimpanzees to
mindread than to behavior-read because a mindreading model has fewer
adjustable parameters given that mental state attribution can unify
multiple inputs and outputs, and Simon Fitzpatrick (2009), who offers
an analysis of the role simplicity plays on both sides of the
debate.

4.2.3 Beyond chimpanzees: Mindreading in other species

Recent developments in the study of mindreading in human infants
provide additional methods for studying false belief in other species.
Despite the widely held claim that children do not develop a theory of
mind until about four years old (Wellman et al. 2001),
researchers using spontaneous response tasks claim that false belief
understanding develops in infancy (see Baillargeon et al. 2010
for a review). Preliminary research using the same methods as the ones
used in infant studies has found no evidence of false belief
understanding in macaque monkeys (Ruiz 2010; Martin & Santos
2014). However, using a similar method, Claudia Uller found that
chimpanzees respond like human infants to stimuli adult researchers
interpret as example of goal directed behaviors (Uller 2004, following
Gergely et al. 1995)

A number of studies suggest that corvids demonstrate social cognitive
abilities similar to those of apes (Bugynar et al. 2007; Dally
et al. 2006; Emery & Clayton 2004). For example, research
on scrub-jay caching behavior shows that individuals who have pilfered
another’s cache in the past will privately recache food when a
conspecific observes the original caching, but not if the original
caching was unobserved (Emery & Clayton 2004). Naive scrub jays
did not recache. Emery & Clayton suggest that the jays who do
recache are engaging in experience projection: “they relate
information about their previous experience as a pilferer to the
possibility of future stealing by another individual, and modify their
recovery strategy appropriately” (Emery & Clayton 2004,
1905).

Studies on perceptual understanding have been conducted on rhesus
monkeys (Flombaum & Santos 2005). Similarly to the chimpanzee and
the scrub-jay studies, these experiments set up a naturalistic
competitive situation in which the subject has to predict the behavior
of a competitor. In one version of this study, rhesus macaques from
the island of Cayo Santiago were pitted against human competitors in a
foraging task; two experimenters would approach a lone monkey, and
each would situate himself differently so that the monkey was visible
to one experimenter but not the other. Both experimenters had one
grape. Flombaum and Santos found that monkeys were more likely to
steal grapes from the experimenter who couldn’t see them. They found
similar results for audibility; when given the choice of stealing a
grape in a transparent box covered with bells, or a grape in a
transparent box that was free of noisemakers, the monkeys preferred
the silent food when no one was looking at them. However, when it was
obvious that the monkey was observed, there was no preference for
stealing quiet over noisy grapes (Santos et al. 2006).

While chimpanzees show some sensitivity to intentions and goals,
domestic dogs may be even more attuned to the intentions of humans.
Dogs are able to use the gaze of a human in order to determine where
food is hidden, an ability not demonstrated in the chimpanzee (Hare
et al. 1998; Hare & Tomasello 1999; Miklosi & Topal
2004; Brauer et al. 2006; see Hare & Woods 2013 for a
summary of the research on dog cognition). Dogs appear to be sensitive
to eye gaze in humans, and often make eye contact before initiating
play. One explanation for dogs’ social acuity is that in selecting for
traits that make dogs better human companions, humans inadvertently
bred dogs who are better able to pass theory of mind tasks (Hare et
al. 2002).

Some researchers who have worked closely with bottlenose dolphins
think that the overall body of research on dolphins suggest that they
mindread. Captive dolphins are able to pass object choice tasks, in
which a human informant points to indicate where food is hidden.
Dolphins can use this cue to access the food (Herman et al. 1999;
Tschudin et al 2001). Dolphins also pass mirror self-recognition
tasks, and Diana Reiss thinks this offers evidence that dolphins also
have some mindreading capacity related to empathy, because human
children develop empathy around the same time they recognize
themselves in mirrors (Reiss 2012).

4.3 Mirror Self-recognition

Some research aims to explore what individuals know about their own
minds. One area of much attention has been mirror
self-recognition (MSR). In this paradigm, developed by
psychologist Gordon Gallup, subjects are surreptitiously marked and
then given a mirror. “Passing” the MSR test involves
touching the mark more frequently when there is a mirror available
than when there is not. Gallop argued that passing MSR entails that
the animal has a concept of self (Gallup 1970), though others dispute
this claim. While it was once thought to be a rare behavior, limited
to some of the great apes, today many species have been studied and at
least some positive results have been reported for the following
species:

Species

Study

Chimpanzees

Lin et al. 1992; Swartz & Evans
1991

Gorillas

Shumaker & Swartz 2002

Orangutans

Swartz et al. 1999

Bottlenosed dolphins

Marino et al. 1994; Reiss & Marino
2001

Asian elephant

Plotnik et al. 2006

Magpies

Prior et al. 2008

Rhesus Monkeys

Rajala et al. 2010

Many other species failed to show mirror directed behavior, including
some monkey species, which suggests to some that there is a
corresponding cognitive mechanism that the above species, but not
others, enjoy. However, it has been pointed out that there are
ecological and biological constraints on this test; not all species
are visually oriented, and some find eyes aversive (this was the
explanation for studies that failed to show MSR in gorillas). For a
discussion of these issues, see the collection of articles in
Self-awareness in Animals and Humans (Parker et al.
1994).

4.4 Metacognition, Memory, and Uncertainty Monitoring

Research on metacognition is another area of research that aims to
investigate the understanding of one’s own mental state (Beran et al. 2012; Crystal and Foote 2009; Shettleworth & Sutton 2006; see also Proust 2013 for a discussion of metacognition in primates). Metacognition is related to self-knowledge as well as to consciousness (see self-consciousness and metacognition in the Animal Cognition entry.

For example, those who knows what they do
and do not know demonstrate metacognition about their epistemic
states. Several nonverbal tests for uncertainty monitoring have
been developed for use with different species. The paradigm might go
as follows: subjects are trained to indicate whether a stimulus is the
same as or different from a sample. When the subjects respond
correctly, they are rewarded with food, but food is taken away when
they give incorrect responses. Once the subjects are trained on this
task, the paradigm is modified to introduce a “bail out”
key with the function of starting a new trial without supplying either
reward or punishment. Interspersed with the easy stimuli are ambiguous
stimuli that the subject is unable to accurately categorize above a
chance level. If the subjects learn to choose the “bail
out” key when they are uncertain, it is thought to indicate that
the subjects are aware of their epistemic state. It has been reported
that many species choose the “bail out” key in such a way
as to maximize rewards, including dolphins (Smithe et al.
1995), rhesus monkeys (Hampton 2001), great apes (Call & Carpenter
2001), and human infants (Call & Carpenter 2001). Mixed results
have been reported with pigeons (Sole et al. 2003).

Memory monitoring can also be involved in some metacognitive tasks. The psychologist Robert Hampton found that rhesus macaques know whether or not they can remember seeing an image. After training monkeys on a simple delayed match to sample task, Hampton (2001) allowed monkeys to decide whether or not to take the test. If they took the test and passed, they received a valuable treat, but if they failed the task they received nothing. If, on the other hand, they decided not to take the test they were given a lesser value food reward. Hampton found that the frequency with which the monkey chose not to take the test increased with the duration of the delay since the presentation of the sample, and that monkeys were able to maximize their rewards by correctly judging when they could pass the task. In a more recent set of studies, Hampton and his students tested seven alternate hypotheses, and concluded that the best explanation of their findings is that the monkeys are monitoring their memory states (Basile et al. 2015).

Though such tests have been designed to test for metacognition, Peter
Carruthers argues that animals can come to solve the problems without
engaging in second-order reasoning. He suggests that the animal could
be operating over beliefs and desires of different strengths, and that
standard practical reasoning systems can be used to output different
responses to the different permutations of weak and strong beliefs and
desires (Carruthers 2008). Alternatively, animals might base their choices on their affective states rather than metacognitive representational states (Carruthers and Ritchie 2012). However, such responses may be based on different conceptions of the nature of metacognition. Strength of belief and affective feelings are both commonly discussed as instances of metacognition in the psychology literature. For example, psychologists have created metacognitive models to show that the strength of response traces can be used to solve metacognitive problems (Smith et al. 2008). In addition, research on human metacognitive capacities study the feeling of rightness as a metacognitive judgment. For example, Valerie Thompson finds that the feeling of rightness of an initial answer predicts both response time and likelihood of switching from the original answer (Thompson et al. 2011).

4.5 Moral Practice

Recently there has been a resurgence of interest in examining whether
other species share with humans any of the faculties involved in
morality or normative engagement.

The view that animals can be full blown moral agents is defended by
Bekoff & Pierce (2009) who argue that some species have a distinct
form of morality that is not a precursor to human morality. Because
they take ‘morality’ to mean “a suite of other-regarding
behaviors that cultivate and regulate complex interactions within
social groups” (Bekoff & Pierce 2009, 82), they take the
complexity of animal behavior, social organization, and cognitive
flexibility to demonstrate that other species have morality in this
sense. Central to the view is that different species have different
norms, and that this makes animal morality species-relative. Despite
the differences, they claim that the important similarities between
species include the capacities for empathy, altruism, cooperation and
perhaps a sense of fairness. Whether or not such claims about animal
capacities are true is a matter of much current research.

Others are more circumspect. Mark Rowlands (2012) argues that because
animals can be have moral emotions, and that those moral emotions
provide moral reasons for an animal’s actions, animals can engage in
moral practice by being moral subjects. Moral subjects are able to act
for moral reasons, but they are not moral agents who are responsible
for their actions, because they can’t consider those moral reasons,
or act to change them. Animals can act for reasons whose content
includes moral emotions, so on his view we can seek evidence for
animal morality by looking to see if animals can emotionally, and
reliably, respond to morally salient features of their situations.

Frans de Waal (1997, 2006, 2009) also takes emotion and empathy to
play a key role in human morality, and he takes the evidence of
empathy and reciprocity in other animals to indicate that morality
evolved, such that human moral behavior is a development of older
capacities we see in other species. We see empathy in other animals
when they offer help to another that is different from the sort of
help the actor needs themselves. De Waal gives a number of examples
that are discussed in the next section. Reciprocity is seen in other
animals when they treat others differently depending on the way they
treat us. He interprets his empirical research on economic games in
chimpanzees (Proctor et al. 2013) and fairness in capuchin
monkeys (de Waal & Brosnan 2003) as evidence of reciprocity and
empathy in other primates.

Furthermore, Gary Varner (2012) argues that while animals are not
full-fledged persons, some animals hold the moral status of near
persons. Persons have cognitive capacities Varner thinks all animals
lack: a narrative sense of self, which requires rationality,
self-consciousness, and autonomy in the sense of having second-order
desires, and the ability to think about one’s self, birth, death,
and personality. To have these capacities, Varner argues, one needs
language. Instead of being persons, animals such as chimpanzees,
dolphins, elephants and scrub jays are near-persons; rats, monkeys and
parrots might be near-persons, too. Near-persons can engage in past
and future thinking, so they can consciously re-experience events and
make plans. This ability gives near-persons additional opportunity for
happiness, because they can re-experience pleasurable experiences, and
unhappiness, because they can dread an unpleasant future. In addition,
a near-person has a present sense of self insofar as they can
mindread, and Varner concludes that monkeys, apes elephants, dolphins,
and scrub jays have the ability to attribute perceptual states to
others.

4.5.1 Emotion and Empathy

Social cognition isn’t limited to knowing the reasons another has for
acting; it can include understanding the
emotions
of others. Among social species, awareness of others’ emotions can
play a role in regulating social interactions, coordinating behavior,
forming bonds between mothers and infants, as well as in forming
short-term coalitions and long lasting relationships.

At least since Darwin’s The Expression of the Emotions in Man and
Animals, facial expressions have been of interest because they
can indicate individuals’ affective states. Research from both the
field and the lab suggests that facial expressions have the same kinds
of functions for chimpanzees and humans (e.g. van Hoof 1967, 1972;
Goodall 1986; Parr 2001). Just as Paul Ekman argued for universality
in emotional expressions among humans across cultures (Ekman et
al. 1969), animal researchers have argued that at least some human
emotions are also found in chimpanzees, and that chimpanzee facial
expressions are homologous to human facial expressions in morphology
and function. For example, van Hoof has argued that the bare-teeth
display of chimpanzees is homologous to the human smile (van Hoof
1973).

Lisa Parr’s research demonstrates that chimpanzees, like human
infants, are able to categorize facial expressions associated with
different emotional responses. Using a match-to-sample paradigm, Parr
and colleagues have shown that chimpanzees recognize at least five
different facial expressions: bared-teeth display, scream, pant-hoot,
play face, and relaxed-lip face (Parr et al. 1998). Given the
salience of these facial expressions to chimpanzees, Parr and
colleagues have argued that facial expressions are important behaviors
for regulating social relations (Parr & de Waal 1999; Parr et
al. 2000). Current research in Parr’s lab is focused on the
development of ChimpFACS (Facial Action Coding System − see
Other Internet Resources) modeled after Ekman’s work in emotion in
human facial expressions. They are using the ChimpFACS to construct
models of chimpanzee expressions in order to determine the
configuration of muscle movements that the chimpanzees find salient in
their perception of emotion.

Another area of study in animal emotion focuses on stress. As a
response to potentially dangerous situations, stress is thought to be
an adaptive emotion in the short term for humans and other animals.
Stress is measured physiologically via levels of glucocorticoid
hormones such as cortisol. In humans,cortisol levels are correlated
with stress levels, and researchers have studied stressors such as
dominance and status ranking in a number of different species
(Sapolsky 2005; Abbott et al. 2003). For example, among baboons
stress in the form of elevated glucocorticoid levels has been
documented in females for about a month after the death of a close
relative, in nursing mothers when a potentially infanticidal immigrant
male arrives, in females when the female ranking system is undergoing
instability, and in males when the male ranking system is unstable
(Cheney & Seyfarth 2007). However, baboons do not appear to
experience stress in the face of another’s stress. Females are not
concerned about male rank instability, and the death of a cohort’s
infant does not raise stress levels in anyone but the mother. It is
suggested that for the baboon, stress is personal and egocentric, and
the lack of sensitivity to the stress of others might be indicative of
a lack of empathy as well (Cheney & Seyfarth 2007).

Emotion in other animals is being studied using brain imaging
techniques such as fMRI and PET. Researchers interested in PTSD and
stress study the brains of monkeys, rats, and mice (Marzluff and
Angell 2012); using PET scans, John Marzluff and colleagues can see
that different parts of the crow brain are active when that crow sees
a known dangerous person compared to seeing a known kind person, and
that these brain regions are homologous to the regions of the brain
that are active in human in the same types of emotional situations.
Marzluff concludes that birds feel fear in a way similar to the way
humans experience fear (Marzluff et al. 2012).

Some research in
moral psychology
suggests that empathy is a necessary component of moral agency, and
animal cognition researchers have been examining whether any other
species share this ability with humans. Empathy is thought to require
the same cognitive sophistication as does understanding another’s
mental state or intention, but, in addition, it requires an affective
response to that mental state. While the terms ‘empathy’ (sharing the
mental state of another) and ‘sympathy’ (a friendly feeling in
response to another’s mental state) have distinct meanings in the
history of psychology, folk psychology, and ethical theory, in animal
cognition research the senses are often blurred and the terms are used
interchangeably.

Early reports of chimpanzee empathy came from Russian comparative
psychologist N. N. Ladygina-Kohts, who raised a chimpanzee named Joni
in the early 20th century (Ladygina-Kohts 2002). More
recently, Sanjida O’Connell analyzed thousands of qualitative reports
of primate responses to the distress of others, and her results
suggest that apes give complex responses in the face of others’
emotions, compared to the responses of monkeys in similar situations
(O’Connell 1995). Studies of chimpanzee behavior performed by Frans de
Waal and his colleagues suggest that chimpanzees understand emotions,
and respond to different emotional states with different behavior,
e.g. consoling the loser of a fight, helping, etc. De Waal takes these
behaviors to be evidence of empathy in chimpanzees (de Waal 2006).

4.5.2 Cooperation

Much of the research on empathy and altruism in other species examines
helping behavior. Cooperation and helping between humans is
ubiquitous, even when it requires that the actor suffer a cost, and
even when the recipient is a stranger. However, because helping can be
engaged in without empathy, and empathic helping requires additional
cognitive resources (e.g. knowledge about how to help someone achieve
their goal and the motivation to act on that knowledge), it is
important to understand the limitations of the data.

Reports of naturalistic animal behavior suggest that many nonhuman
animal species engage in prosocial behavior that may be empathic or
proto-empathic in nature (de Waal 1996). De Waal often presents the
famous example of Binti-Jua, a female gorilla at the Brookfield zoo in
Chicago, who made the news when she rescued a 3-year-old human boy who
fell into her habitat. Binti-Jua cradled the boy in her arms before
handing him to a zookeeper. However, critics of the prosocial
interpretation of Binti-Jua’s behavior suggest that given her early
exposure to doll play, an associative learning explanation is also
possible.

Others claim that what may look like prosocial behavior may instead be
a way of eliminating aversive stimuli. For example, research on rats
and rhesus monkeys has shown that both species will cease eating when
doing so causes shocks to a conspecific in an adjoining cage
(Masserman et al. 1964). Masserman reports that one rhesus
monkey almost starved himself to death to avoid shocking another.
Alternatively, helping behavior among kin may be explained
noncognitively as
biological altruism.
To determine whether other species engage in helping behavior that
cannot be explained by other mechanisms, researchers have developed
paradigms to determine whether chimpanzees display helping behavior to
unrelated individuals. Chimpanzees are thought to be an especially
good species to examine, given the range of cooperative behaviors they
naturally perform, such as hunting (Boesch 2002), border patrolling
(Mitani 2002), and coalition building (de Wall 1982). Cooperation
among chimpanzees (Hirata 2003; Melis et al. 2006) and bonobos
(Hare et al. 2007) has been demonstrated in a food-sharing
task, but chimpanzees are thought to cooperate only when the dyads are
generally tolerant of one another (Hare et al. 2007).

In the last few years, a host of experimental studies have
investigated the contexts in which chimpanzees will act to help
others, and compared chimpanzee behavior with human behavior.
Differences and similarities have been found, suggesting to some that
the evolutionary roots of helping behavior may be different in
chimpanzees and humans (Brosnan & Bshary 2010; Greenberg et
al. 2010; Melis and Semmann 2010; Yamamoto & Tanaka 2009). In
one set of studies, Warneken and colleagues compared the helping
behavior of human infants and chimpanzees in a social setting. The
experimenter made nonverbal requests for help, thus achieving a goal
such as picking up a dropped object or opening a box. While both 18
month-old children and chimpanzees respond to simple requests (e.g.
picking up a dropped object), children are able to respond to more
complex requests (Warneken & Tomasello 2006, video from these
studies is available here). Other studies found that chimpanzees help
humans or conspecifics open doors to acquire food (Warneken et
al. 2007; Melis et al. 2008), and respond to conspecific
requests to bring a needed tool (Yamamoto et al. 2009).
Chimpanzees even help a conspecific gain food after they have already
been rewarded, without the need for a request for assistance
(Greenberg et al. 2010).

Other experiments failed to elicit any helping behavior, even when
helping required no additional effort on the actor’s part (Silk et al.
2005; Jensen et al. 2006; Vonk et al. 2008). In one
study, the experimental setup allowed the actor to pull one of two
ropes, one of which delivered food to only the actor, and the other of
which delivered food to an adjacent chimpanzee as well as the actor
(Silk et al. 2005). No preference was found for pulling the
rope that rewarded both animals, even though the actor could see and
hear the other chimpanzee. There was no significant correlation
between another animal being present and which rope was pulled. Silk
and colleagues conclude that “The absence of other-regarding
preferences in chimpanzees may indicate that such preferences are a
derived property of the human species, tied to sophisticated
capacities for cultural learning, theory of mind, perspective taking
and moral judgment” (Silk et al. 2005, 1359).

Several explanations have been offered for the negative result. Some
suggest the chimpanzees may have been distracted by the presence of
food (Warneken & Tomasello 2006), or that they are more likely to
help in response to a request for help (Yamamoto 2009). However,
additional experimental results find that chimpanzees will help
conspecifics acquire food even when there isn’t a request for
assistance; instead, the design of the studies may have elicited
chimpanzee’s competitive nature (Greenberg et al. 2010).

The conclusion that some primate species will cooperate in some
contexts has led to a wider investigation into the situations the lead
to cooperation. Byrnit and colleagues (2015) found that chimpanzees
and bonobos tended to share high value food more than low value food.
Suchak et al (2014) found that chimpanzees spontaneously engage
in joint action, and particularly prefer to cooperate with kin or
nonkin of similar rank. Molesti and Majolo (2016) found that wild
Barbary macaques are more likely to cooperate with a partner they are
known to affiliate with. And, in a large study of 15 primate species,
researchers found that cooperative breeding is the best predictor for
increased prosocial behavior. They conclude that human
hyper-cooperation we see today most likely evolved from the hominid
move toward cooperative childcare (Burkart et al 2014). In
response to studies and observations that great apes and other
primates do cooperate in some cases, Michael Tomasello insists that
for chimpanzees, "the key to understanding their cooperation is this
same overarching matrix of social competition" (2016, 23).

4.5.3 Punishment

Punishment
might be considered the flipside of helping. Like altruistic
behaviors, acts of punishment may lower individual fitness, so the
existence of animal punishment is a puzzle for biologists. Across many
species, individuals engage in aggressive acts in response to
violations of their interests. However, game theoretical analysis
suggests that punishment is an adaptive strategy for individuals
living in stable social groups, and that punishment can help establish
or secure dominance in a social group (Clutton-Brock & Parker
1995).

Experimental studies of primate responses to violations of their
interests (or of group norms) have uncovered mixed results. For
example, while capuchin monkeys will withdraw and stop participating
in a task if they observe another monkey getting a better reward for
the same task (Brosnan & de Waal 2003), chimpanzees do not reject
an unequal division of resources in an ultimatum game (Jensen et
al. 2007) (see philosophy of economics). Jensen and colleagues
suggest that chimpanzees may not be concerned with fairness, and are
instead rational maximizers of resources. In response it has been
argued that just as what counts as fair differs among human
communities, what is unfair to humans may not be unfair to another
species; researchers should consider natural behavior in order to
uncover potential fairness norms (Bekoff & Pierce 2009; Brosnan
& Bshary 2010).

Other studies have directly tested punishment in chimpanzees. While
there is evidence that chimpanzees will punish a thief who stole food
from him (Jensen et al. 2007), currently there are only
negative findings on the issue of third party punishment (Jensen et
al. 2007; Riedl et al. 2010); in an experimental setting,
even mothers will not retaliate against an individual who steals food
from their offspring. However, since some field researchers report
incidents of third party punishment in the wild, further research is
required.

Experiments designed to examine the evolutionary roots of social norms
are also suggestive. Chimpanzees look longer at video clips of
infanticide than at other clips with striking stimuli but lacking norm
violations (von Rohr et al. 2010). Whether such findings
suggest that normative elements are present in animal communities is
an issue that requires further investigation. Many questions remain to
be answered (and asked). The mechanisms of altruism, cooperation and
punishment, the existence of social norms, the affective requirements
of moral reasoning are all issues that require conceptual analysis as
well as empirical investigation of prosocial behavior in humans and
other animals.

5. Animal Cognition and Philosophy: What Next?

Today we are in a kind of golden era when it comes to animal cognition
research. Different species are being studied in the field and in the
lab, and the results of these studies may be relevant to areas of
philosophy including action theory, agency, belief, concepts,
consciousness, culture, epistemology, ethics, folk psychology,
imagery, language, memory, mental causation, mental content,
modularity of mind, perception, personal identity, practical reason,
rationality, and so forth. It seems that every day a new report is
released, and many of these seem to have some theoretical
implications.

Of course, scientific reports must be examined carefully to
distinguish between methodologically solid findings and unwarranted
interpretations, and it goes without saying that popular media reports
of these studies are sometimes misleading. The epistemology of animal
cognition research has been one area with much recent activity (see
e.g. Buckner 2013; Clatterbuck 2015; Fitzpatrick 2008; Halina 2015;
Meketa 2014; Sober 2015; Starzak 2016).

Philosophy of animal cognition, as a subfield of philosophy of
science, is one place where such methodological questions can be
examined. For further reading in this area, see Kristin Andrews’
(2015) The Animal Mind: An Introduction to the Philosophy of
Animal Cognition, Colin Allen and Marc Bekoff’s book Species
of Mind: The Philosophy and Biology of Cognitive Ethology, and
the collection The Philosophy of Animal Minds edited by
Robert Lurz. For an introduction to an evolutionary psychological
approach to studying animal cognition, see Sara Shettleworth’s
Cognition, Evolution, and Behavior. For an introduction to
the ethological approach to studying animals, see Philip Lehner’s
Handbook of Ethological Methods.

Seyfarth R.M. & Cheney, D.L. (2015). “The evolution of
concepts about agents: Or what do animals recognize when they
recognize an agent?” In The Conceptual Mind: New Directions
in the Study of Concepts. (Margolis, E., Laurence, S.,
Eds.):57–76., Cambridge, MA: MIT Press.

–––. (1973). “A Structural Analysis of the
Social Behaviour of a Semi-Captive Group of Chimpanzees.” In
Cranach, M.V., Vine, I. (eds.), Social Communication and Movement.
European Monographs in Social Psychology 4. London: Academic.
75–162.

Wilson, M. (2008). “How did we get from there to here? An
evolutionary perspective on embodied cognition.” In Paco Calvo
& Toni Gomila (eds.), Handbook of Cognitive Science: An
Embodied Approach. San Diego: Elsevier.